{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9be0bacc",
   "metadata": {},
   "source": [
    "# 4 编写结构化程序\n",
    "\n",
    "现在，你对Python 编程语言处理自然语言的能力已经有了体会。不过，如果你是Python或者编程新手，你可能仍然要努力对付Python，而尚未感觉到你在完全控制它。在这一章中，我们将解决以下问题：\n",
    "\n",
    "1. 怎么能写出结构良好、可读的程序，你和其他人将能够很容易的重新使用它？\n",
    "2. 基本结构块，如循环、函数以及赋值，是如何执行的？\n",
    "3. Python 编程的陷阱有哪些，你怎么能避免它们吗？\n",
    "\n",
    "一路上，你将巩固基本编程结构的知识，了解更多关于以一种自然和简洁的方式使用Python语言特征的内容，并学习一些有用的自然语言数据可视化技术。如前所述，本章包含许多例子和练习（和以前一样，一些练习会引入新材料）。编程新手的读者应仔细做完它们，并在需要时查询其他编程介绍；有经验的程序员可以快速浏览本章。\n",
    "\n",
    "在这本书的其他章节中，为了讲述NLP的需要，我们已经组织了一些编程的概念。在这里，我们回到一个更传统的方法，材料更紧密的与编程语言的结构联系在一起。这里不会完整的讲述编程语言，我们只关注对NLP最重要的语言结构和习惯用法。\n",
    "\n",
    "<a href=\"#41-回到基础\">4.1 回到基础</a>\n",
    "\n",
    "<a href=\"#42-序列\">4.2 序列</a>\n",
    "\n",
    "<a href=\"#43-风格的问题\">4.3 风格的问题</a>\n",
    "\n",
    "<a href=\"#44-函数-结构化编程的基础\">4.4 函数：结构化编程的基础</a>\n",
    "\n",
    "<a href=\"#45-更多关于函数\">4.5 更多关于函数</a>\n",
    "\n",
    "<a href=\"#46-程序开发\">4.6 程序开发</a>\n",
    "\n",
    "<a href=\"#47-算法设计\">4.7 算法设计</a>\n",
    "\n",
    "## 4.1 回到基础\n",
    "\n",
    "<a href=\"#赋值\">1. 赋值</a>\n",
    "\n",
    "<a href=\"#等式\">2. 等式</a>\n",
    "\n",
    "<a href=\"#条件\">3. 条件</a>\n",
    "\n",
    "### 赋值\n",
    "\n",
    "赋值似乎是最基本的编程概念，不值得单独讨论。不过，也有一些令人吃惊的微妙之处。思考下面的代码片段："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98877b18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Monty'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foo = 'Monty'\n",
    "bar = foo # 1\n",
    "foo = 'Python' # 2\n",
    "bar"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "719bd366",
   "metadata": {},
   "source": [
    "这个结果与预期的完全一样。当我们在上面的代码中写`bar = foo`时 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#assignment1)，`foo`的值（字符串`'Monty'`）被赋值给`bar`。也就是说，`bar`是`foo`的一个副本，所以当我们在第 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#assignment2)行用一个新的字符串`'Python'`覆盖`foo`时，`bar`的值不会受到影响。\n",
    "\n",
    "然而，赋值语句并不总是以这种方式复制副本。赋值总是一个表达式的值的复制，但值并不总是你可能希望的那样。特别是结构化对象的“值”，例如一个列表，实际上是一个对象的引用。在下面的例子中， [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#assignment3)将`foo`的引用分配给新的变量`bar`。现在，当我们在 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#assignment4)行修改`foo`内的东西，我们可以看到`bar`的内容也已改变。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1ac21189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Monty', 'Bodkin']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foo = ['Monty', 'Python']\n",
    "bar = foo # 1\n",
    "foo[1] = 'Bodkin' # 2\n",
    "bar"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "5251a794",
   "metadata": {},
   "source": [
    "![64864d38550248d5bd9b82eeb6f0583b.png](attachment:64864d38550248d5bd9b82eeb6f0583b.png)\n",
    "\n",
    "图 4.1：列表赋值与计算机内存：两个列表对象`foo`和`bar`引用计算机内存中的相同的位置；更新`foo`将同样修改`bar`，反之亦然。\n",
    "\n",
    "`bar = foo` [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#assignment3)行并不会复制变量的内容，只有它的“引用对象”。要了解这里发生了什么事，我们需要知道列表是如何存储在计算机内存的。在[4.1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#fig-array-memory)中，我们看到一个列表`foo`是对存储在位置3133处的一个对象的引用（它自身是一个指针序列，其中的指针指向其它保存字符串的位置）。当我们赋值`bar = foo`时，仅仅是3133位置处的引用被复制。这种行为延伸到语言的其他方面，如参数传递（[4.4](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#sec-functions)）。\n",
    "\n",
    "让我们做更多的实验，通过创建一个持有空列表的变量`empty`，然后在下一行使用它三次。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32e6cdf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[], [], []]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty = []\n",
    "nested = [empty, empty, empty]\n",
    "nested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c6c3aa2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Python'], ['Python'], ['Python']]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nested[1].append('Python')\n",
    "nested"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e82d9016",
   "metadata": {},
   "source": [
    "请看，改变列表中嵌套列表内的一个项目，它们全改变了。这是因为三个元素中的每一个实际上都只是一个内存中的同一列表的引用。\n",
    "\n",
    "注意\n",
    "\n",
    "**轮到你来：** 用乘法创建一个列表的列表：`nested = [[]] * 3`。现在修改列表中的一个元素，观察所有的元素都改变了。使用Python的`id()`函数找出任一对象的数字标识符， 并验证`id(nested[0])`，`id(nested[1])`与`id(nested[2])`是一样的。\n",
    "\n",
    "现在请注意，当我们分配一个新值给列表中的一个元素时，它并不会传送给其他元素："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7168e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Python'], ['Monty'], ['Python']]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nested = [[]] * 3\n",
    "nested[1].append('Python')\n",
    "nested[1] = ['Monty']\n",
    "nested"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31355d5e",
   "metadata": {},
   "source": [
    "我们一开始用含有3个引用的列表，每个引用指向一个空列表对象。然后，我们通过给它追加`'Python'`修改这个对象，结果变成包含3个到一个列表对象`['Python']`的引用的列表。下一步，我们使用到一个新对象`['Monty']`的引用来*覆盖*三个元素中的一个。这最后一步修改嵌套列表内的3个对象引用中的1个。然而，`['Python']`对象并没有改变，仍然是在我们的嵌套列表的列表中的两个位置被引用。关键是要明白通过一个对象引用修改一个对象与通过覆盖一个对象引用之间的区别。\n",
    "\n",
    "注意\n",
    "\n",
    "**重要：** 要从列表`foo`复制项目到一个新的列表`bar`，你可以写`bar = foo[:]`。这会复制列表中的对象引用。若要复制结构而不复制任何对象引用，请使用`copy.deepcopy()`。\n",
    "\n",
    "### 等式\n",
    "\n",
    "Python提供两种方法来检查一对项目是否相同。`is`操作符测试对象的ID。我们可以用它来验证我们早先的对对象的观察。首先，我们创建一个列表，其中包含同一对象的多个副本，证明它们不仅对于`==`完全相同，而且它们是同一个对象："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d6e7587b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size = 5\n",
    "python = ['Python']\n",
    "snake_nest = [python] * size\n",
    "snake_nest[0] == snake_nest[1] == snake_nest[2] == snake_nest[3] == snake_nest[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "21a33fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake_nest[0] is snake_nest[1] is snake_nest[2] is snake_nest[3] is snake_nest[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9a0797",
   "metadata": {},
   "source": [
    "现在，让我们将一个新的python放入嵌套中。我们可以很容易地表明这些对象不完全相同:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44b53518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Python'], ['Python'], ['Python'], ['Python'], ['Python']]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "position = random.choice(range(size))\n",
    "snake_nest[position] = ['Python']\n",
    "snake_nest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "90d2bbed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake_nest[0] == snake_nest[1] == snake_nest[2] == snake_nest[3] == snake_nest[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d895d657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake_nest[0] is snake_nest[1] is snake_nest[2] is snake_nest[3] is snake_nest[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9823ff3a",
   "metadata": {},
   "source": [
    "你可以再做几对测试，发现哪个位置包含闯入者，函数`id()`使检测更加容易："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e3f17164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2322472289984, 2322472289984, 2322472289984, 2322472231040, 2322472289984]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[id(snake) for snake in snake_nest]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da4ff26b",
   "metadata": {},
   "source": [
    "这表明列表中的第二个项目有一个独特的标识符。如果你尝试自己运行这段代码，请期望看到结果列表中的不同数字，以及闯入者可能在不同的位置。\n",
    "\n",
    "有两种等式可能看上去有些奇怪。然而，这真的只是类型与标识符式的区别，与自然语言相似，这里在一种编程语言中呈现出来。\n",
    "\n",
    "### 条件\n",
    "\n",
    "在`if`语句的条件部分，一个非空字符串或列表被求值为真，而一个空字符串或列表的被求值为假。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6eea039",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat\n",
      "['dog']\n"
     ]
    }
   ],
   "source": [
    "mixed = ['cat', '', ['dog'], []]\n",
    "for element in mixed:\n",
    "    if element:\n",
    "        print(element)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef8ceaad",
   "metadata": {},
   "source": [
    "也就是说，我们*不必*在条件中写`if len(element) > 0:`。\n",
    "\n",
    "使用`ifelif`而不是在一行中使用两个`if`语句有什么区别？嗯，考虑以下情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c920ae2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "animals = ['cat', 'dog']\n",
    "if 'cat' in animals:\n",
    "    print(1)\n",
    "elif 'dog' in animals:\n",
    "    print(2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1315d564",
   "metadata": {},
   "source": [
    "因为表达式中`if`子句条件满足，Python就不会求值`elif`子句，所以我们永远不会得到输出`2`。相反，如果我们用一个`if`替换`elif`，那么我们将会输出`1`和`2`。所以`elif`子句比单独的`if`子句潜在地给我们更多信息；当它被判定为真时，告诉我们不仅条件满足而且前面的`if`子句条件*不*满足。\n",
    "\n",
    "`all()`函数和`any()`函数可以应用到一个列表（或其他序列），来检查是否全部或任一项目满足某个条件："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "777c350c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent = ['No', 'good', 'fish', 'goes', 'anywhere', 'without', 'a', 'porpoise', '.']\n",
    "all(len(w) > 4 for w in sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8007f560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "any(len(w) > 4 for w in sent)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e7f1cb9b",
   "metadata": {},
   "source": [
    "## 4.2 序列\n",
    "\n",
    "<a href=\"#序列类型上的操作\">1. 序列类型上的操作</a>\n",
    "\n",
    "<a href=\"#合并不同类型的序列\">2. 合并不同类型的序列</a>\n",
    "\n",
    "<a href=\"#生成器表达式\">3. 生成器表达式</a>\n",
    "\n",
    "到目前为止，我们已经看到了两种序列对象：字符串和列表。另一种序列被称为元组。元组由逗号操作符 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#create-tuple)构造，而且通常使用括号括起来。实际上，我们已经在前面的章节中看到过它们，它们有时也被称为“配对”，因为总是有两名成员。然而，元组可以有任何数目的成员。与列表和字符串一样，元组可以被索引 # [2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#index-tuple)和切片 # [3](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#slice-tuple)，并有长度 # [4](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#length-tuple)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b254dae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('walk', 'fem', 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = 'walk', 'fem', 3 # 1\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7eb7c04d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'walk'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[0] # 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a9a4bfd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('fem', 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[1:] # 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8104cdb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(t) # 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd371541",
   "metadata": {},
   "source": [
    "小心！\n",
    "\n",
    "元组使用逗号操作符来构造。括号是一个Python语法的一般功能，设计用于分组。定义一个包含单个元素`'snark'`的元组是通过添加一个尾随的逗号，像这样：\"`'snark',`\"。空元组是一个特殊的情况下，使用空括号`()`定义。\n",
    "\n",
    "让我们直接比较字符串、列表和元组，在各个类型上做索引、切片和长度操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c274bd91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('t', 'the', 'turned')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw = 'I turned off the spectroroute'\n",
    "text = ['I', 'turned', 'off', 'the', 'spectroroute']\n",
    "pair = (6, 'turned')\n",
    "raw[2], text[3], pair[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e908599c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('ute', ['off', 'the', 'spectroroute'], (6, 'turned'))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw[-3:], text[-3:], pair[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c8020ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29, 5, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(raw), len(text), len(pair)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9479c650",
   "metadata": {},
   "source": [
    "请注意在此代码示例中，我们在一行代码中计算多个值，中间用逗号分隔。这些用逗号分隔的表达式其实就是元组——如果没有歧义，Python允许我们忽略元组周围的括号。当我们输出一个元组时，括号始终显示。通过以这种方式使用元组，我们隐式的将这些项目聚集在一起。\n",
    "\n",
    "### 序列类型上的操作\n",
    "\n",
    "我们可以用多种有用的方式遍历一个序列`s`中的项目，如 [4.1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#tab-python-sequence)所示。\n",
    "\n",
    "表 4.1：\n",
    "\n",
    "遍历序列的各种方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c598b033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[',', '.', 'Red', 'lorry', 'red', 'yellow']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk import word_tokenize # 导入 nltk 库\n",
    "\n",
    "raw = 'Red lorry, yellow lorry, red lorry, yellow lorry.'\n",
    "text = word_tokenize(raw) # word_tokenize 的作用是分词\n",
    "fdist = nltk.FreqDist(text) #  FreqDist 频差\n",
    "sorted(fdist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c553e0b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lorry: 4; ,: 3; yellow: 2; Red: 1; red: 1; .: 1; "
     ]
    }
   ],
   "source": [
    "for key in fdist:\n",
    "    print(key + ':', fdist[key], end='; ')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fbd2f8b",
   "metadata": {},
   "source": [
    "在接下来的例子中，我们使用元组重新安排我们的列表中的内容。（可以省略括号，因为逗号比赋值的优先级更高。）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5e86204c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['I', 'turned', 'the', 'spectroroute', 'off']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = ['I', 'turned', 'off', 'the', 'spectroroute']\n",
    "words[2], words[3], words[4] = words[3], words[4], words[2]\n",
    "words"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a03584",
   "metadata": {},
   "source": [
    "这是一种地道和可读的移动列表内的项目的方式。它相当于下面的传统方式不使用元组做上述任务（注意这种方法需要一个临时变量`tmp`）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "58df17b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = words[2]\n",
    "words[2] = words[3]\n",
    "words[3] = words[4]\n",
    "words[4] = tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424bc60d",
   "metadata": {},
   "source": [
    "正如我们已经看到的，Python有序列处理函数，如`sorted()`和`reversed()`，它们重新排列序列中的项目。也有修改序列结构的函数，可以很方便的处理语言。因此，`zip()`接收两个或两个以上的序列中的项目，将它们“压缩”打包成单个的配对列表。给定一个序列`s`，`enumerate(s)`返回一个包含索引和索引处项目的配对。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0cf9d97c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x21cc2ccd7c0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = ['I', 'turned', 'off', 'the', 'spectroroute']\n",
    "tags = ['noun', 'verb', 'prep', 'det', 'noun']\n",
    "zip(words, tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fe23e381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('I', 'noun'),\n",
       " ('turned', 'verb'),\n",
       " ('off', 'prep'),\n",
       " ('the', 'det'),\n",
       " ('spectroroute', 'noun')]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(words, tags))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1469cecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 'I'), (1, 'turned'), (2, 'off'), (3, 'the'), (4, 'spectroroute')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate(words)) # 枚举"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "82b5a261",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "只在需要的时候进行计算（或者叫做“惰性计算”特性），这是Python 3和NLTK 3的一个普遍特点。当你期望看到一个序列时，如果你看到的却是类似`<zip object at 0x10d005448>`这样的结果， 你可以强制求值这个对象，只要把它放在一个期望序列的上下文中，比如`list(`x`)`或`for item in` x。\n",
    "\n",
    "对于一些NLP任务，有必要将一个序列分割成两个或两个以上的部分。例如，我们可能需要用90％的数据来“训练”一个系统，剩余10％进行测试。要做到这一点，我们指定想要分割数据的位置 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#cut-location)，然后在这个位置分割序列 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#cut-sequence)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6ce2aba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = nltk.corpus.nps_chat.words() # nps_chat 词汇列表的使用\n",
    "cut = int(0.9 * len(text)) # 1\n",
    "training_data, test_data = text[:cut], text[cut:] # 2\n",
    "text == training_data + test_data # 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e8dcab8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(training_data) / len(test_data) # 4"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d8a6d9a8",
   "metadata": {},
   "source": [
    "我们可以验证在此过程中的原始数据没有丢失，也没有重复 [# 3](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#cut-preserve)。我们也可以验证两块大小的比例是我们预期的 [# 4](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#cut-ratio)。\n",
    "\n",
    "### 合并不同类型的序列\n",
    "\n",
    "让我们综合关于这三种类型的序列的知识，一起使用列表推导处理一个字符串中的词，按它们的长度排序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1aeaf74a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I off the turned spectroroute'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = 'I turned off the spectroroute'.split() # 1\n",
    "wordlens = [(len(word), word) for word in words] # 2\n",
    "wordlens.sort() # 3\n",
    "' '.join(w for (_, w) in wordlens) # 4"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "de7356c6",
   "metadata": {},
   "source": [
    "上述代码段中每一行都包含一个显著的特征。一个简单的字符串实际上是一个其上定义了方法如`split()`  [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#string-object)的对象。我们使用列表推导建立一个元组的列表 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#tuple-comprehension)，其中每个元组由一个数字（词长）和这个词组成，例如`(3, 'the')`。我们使用`sort()`方法 [# 3](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#sort-method)就地排序列表。最后，丢弃长度信息，并将这些词连接回一个字符串 [# 4](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#discard-length)。（下划线 [# 4](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#discard-length)只是一个普通的Python变量，我们约定可以用下划线表示我们不会使用其值的变量。）\n",
    "\n",
    "我们开始谈论这些序列类型的共性，但上面的代码说明了这些序列类型的重要的区别。首先，字符串出现在开头和结尾：这是很典型的，我们的程序先读一些文本，最后产生输出给我们看。列表和元组在中间，但使用的目的不同。一个链表是一个典型的具有相同类型的对象的序列，它的长度是任意的。我们经常使用列表保存词序列。相反，一个元组通常是不同类型的对象的集合，长度固定。我们经常使用一个元组来保存一个纪录，与一些实体相关的不同字段的集合。使用列表与使用元组之间的区别需要一些时间来习惯，所以这里是另一个例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "21fa3b8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "lexicon = [\n",
    "    ('the', 'det', ['Di:', 'D@']),\n",
    "    ('off', 'prep', ['Qf', 'O:f'])\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c10879",
   "metadata": {},
   "source": [
    "在这里，用一个列表表示词典，因为它是一个单一类型的对象的集合——词汇条目——没有预定的长度。个别条目被表示为一个元组，因为它是一个有不同的解释的对象的集合，例如正确的拼写形式、词性、发音（以SAMPA计算机可读的拼音字母表示，`http://www.phon.ucl.ac.uk/home/sampa/`）。请注意，这些发音都是用列表存储的。（为什么呢？）\n",
    "\n",
    "注意\n",
    "\n",
    "决定何时使用元组还是列表的一个好办法是看一个项目的内容是否取决与它的位置。例如，一个已标注的词标识符由两个具有不同解释的字符串组成，我们选择解释第一项为词标识符，第二项为标注。因此，我们使用这样的元组：`('grail', 'noun')`；一个形式为`('noun', 'grail')`的元组将是无意义的，因为这将是一个词`noun`被标注为`grail`。相反，一个文本中的元素都是词符， 位置并不重要。因此， 我们使用这样的列表：`['venetian', 'blind']`；一个形式为`['blind', 'venetian']`的列表也同样有效。词的语言学意义可能会有所不同，但作为词符的列表项的解释是不变的。\n",
    "\n",
    "列表和元组之间的使用上的区别已经讲过了。然而，还有一个更加基本的区别：在Python中，列表是可变的，而元组是不可变的。换句话说，列表可以被修改，而元组不能。这里是一些在列表上的操作，就地修改一个列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "52761fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "lexicon.sort()\n",
    "lexicon[1] = ('turned', 'VBD', ['t3:nd', 't3`nd'])\n",
    "del lexicon[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fc09313",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "**轮到你来：**使用`lexicon = tuple(lexicon)`将`lexicon`转换为一个元组，然后尝试上述操作，确认它们都不能运用在元组上。\n",
    "\n",
    "### 生成器表达式\n",
    "\n",
    "我们一直在大量使用列表推导，因为用它处理文本结构紧凑和可读性好。下面是一个例子，分词和规范化一个文本："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "89098997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['``',\n",
       " 'when',\n",
       " 'i',\n",
       " 'use',\n",
       " 'a',\n",
       " 'word',\n",
       " ',',\n",
       " \"''\",\n",
       " 'humpty',\n",
       " 'dumpty',\n",
       " 'said',\n",
       " 'in',\n",
       " 'rather',\n",
       " 'a',\n",
       " 'scornful',\n",
       " 'tone',\n",
       " ',',\n",
       " \"''\",\n",
       " 'it',\n",
       " 'means',\n",
       " 'just',\n",
       " 'what',\n",
       " 'i',\n",
       " 'choose',\n",
       " 'it',\n",
       " 'to',\n",
       " 'mean',\n",
       " '-',\n",
       " 'neither',\n",
       " 'more',\n",
       " 'nor',\n",
       " 'less',\n",
       " '.',\n",
       " \"''\"]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = '''\"When I use a word,\" Humpty Dumpty said in rather a scornful tone,\n",
    "\"it means just what I choose it to mean - neither more nor less.\"'''\n",
    "[w.lower() for w in word_tokenize(text)]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9a7c016a",
   "metadata": {},
   "source": [
    "假设我们现在想要进一步处理这些词。我们可以将上面的表达式插入到一些其他函数的调用中 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#max-comprehension)，Python允许我们省略方括号 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#max-generator)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "66125c86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'word'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max([w.lower() for w in word_tokenize(text)]) # 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f6d04f71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'word'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(w.lower() for w in word_tokenize(text)) # 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "98cc1055",
   "metadata": {},
   "source": [
    "第二行使用了生成器表达式。这不仅仅是标记方便：在许多语言处理的案例中，生成器表达式会更高效。在 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#max-comprehension)中，列表对象的存储空间必须在max()的值被计算之前分配。如果文本非常大的，这将会很慢。在 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#max-generator)中，数据流向调用它的函数。由于调用的函数只是简单的要找最大值——按字典顺序排在最后的词——它可以处理数据流，而无需存储迄今为止的最大值以外的任何值。\n",
    "\n",
    "## 4.3 风格的问题\n",
    "\n",
    "编程是作为一门科学的艺术。无可争议的程序设计的“圣经”，Donald Knuth 的2500页的多卷作品，叫做《计算机程序设计艺术》。已经有许多书籍是关于文学化编程的，它们认为人类，不只是电脑，必须阅读和理解程序。在这里，我们挑选了一些编程风格的问题，它们对你的代码的可读性，包括代码布局、程序与声明的风格、使用循环变量都有重要的影响。\n",
    "\n",
    "<a href=\"#python代码风格\">1. Python代码风格</a>\n",
    "\n",
    "<a href=\"#过程风格与声明风格\">2. 过程风格与声明风格</a>\n",
    "\n",
    "<a href=\"#计数器的一些合理用途\">3. 计数器的一些合理用途</a>\n",
    "\n",
    "### Python代码风格\n",
    "\n",
    "编写程序时，你会做许多微妙的选择：名称、间距、注释等等。当你在看别人编写的代码时，风格上的不必要的差异使其难以理解。因此，Python 语言的设计者发表了Python代码风格指南，http`http://www.python.org/dev/peps/pep-0008/`。风格指南中提出的基本价值是一致性，目的是最大限度地提高代码的可读性。我们在这里简要回顾一下它的一些主要建议，并请读者阅读完整的指南，里面有对实例的详细的讨论。\n",
    "\n",
    "代码布局中每个缩进级别应使用4个空格。你应该确保当你在一个文件中写Python代码时，避免使用tab缩进，因为它可能由于不同的文本编辑器的不同解释而产生混乱。每行应少于80个字符长；如果必要的话，你可以在圆括号、方括号或花括号内换行，因为Python能够探测到该行与下一行是连续的。如果你需要在圆括号、方括号或大括号中换行，通常可以添加额外的括号，也可以在行尾需要换行的地方添加一个反斜杠："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f1ae3ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "if (len(syllables) > 4 and len(syllables[2]) == 3 and\n",
    "    syllables[2][2] in [aeiou] and syllables[2][3] == syllables[1][3]):\n",
    "    process(syllables)\n",
    "if len(syllables) > 4 and len(syllables[2]) == 3 and \\\n",
    "    syllables[2][2] in [aeiou] and syllables[2][3] == syllables[1][3]:\n",
    "    process(syllables)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2403530",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "键入空格来代替制表符很快就会成为一件苦差事。许多程序编辑器内置对Python的支持，能自动缩进代码，突出任何语法错误（包括缩进错误）。关于Python编辑器列表，请见`http://wiki.python.org/moin/PythonEditors`。\n",
    "\n",
    "### 过程风格与声明风格\n",
    "\n",
    "我们刚才已经看到可以不同的方式执行相同的任务，其中蕴含着对执行效率的影响。另一个影响程序开发的因素是*编程风格*。思考下面的计算布朗语料库中词的平均长度的程序："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cd28e665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.401545438271973"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = nltk.corpus.brown.words(categories='news')\n",
    "count = 0\n",
    "total = 0\n",
    "for token in tokens:\n",
    "    count += 1\n",
    "    total += len(token)\n",
    "total / count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cef6381",
   "metadata": {},
   "source": [
    "在这段程序中，我们使用变量`count`跟踪遇到的词符的数量，`total`储存所有词的长度的总和。这是一个低级别的风格，与机器代码，即计算机的CPU 所执行的基本操作，相差不远。两个变量就像CPU的两个寄存器，积累许多中间环节产生的值，和直到最才有意义的值。我们说，这段程序是以*过程*风格编写，一步一步口授机器操作。现在，考虑下面的程序，计算同样的事情："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "65858c7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.401545438271973\n"
     ]
    }
   ],
   "source": [
    "total = sum(len(t) for t in tokens)\n",
    "print(total / len(tokens))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc0915f",
   "metadata": {},
   "source": [
    "第一行使用生成器表达式累加标示符的长度，第二行像前面一样计算平均值。每行代码执行一个完整的、有意义的工作，可以高级别的属性，如：“`total`是标识符长度的总和”，的方式来理解。实施细节留给Python解释器。第二段程序使用内置函数，在一个更抽象的层面构成程序；生成的代码是可读性更好。让我们看一个极端的例子："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e299c4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_list = [] # 这个例子太极端了，跑一分钟还没出结果，跳过\n",
    "i = 0\n",
    "while i < len(tokens):\n",
    "    j = 0\n",
    "    while j < len(word_list) and word_list[j] <= tokens[i]:\n",
    "        j += 1\n",
    "    if j == 0 or tokens[i] != word_list[j-1]:\n",
    "        word_list.insert(j, tokens[i])\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5746a079",
   "metadata": {},
   "source": [
    "等效的声明版本使用熟悉的内置函数，可以立即知道代码的目的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "542b9db3",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_list = sorted(set(tokens))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e464c5e",
   "metadata": {},
   "source": [
    "另一种情况，对于每行输出一个计数值，一个循环计数器似乎是必要的。然而，我们可以使用`enumerate()`处理序列`s`，为`s`中每个项目产生一个`(i, s[i])`形式的元组，以`(0, s[0])`开始。下面我们枚举频率分布的值，生成嵌套的`(rank, (word, count))`元组。按照产生排序项列表时的需要，输出`rank+1`使计数从`1`开始。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ce2aaa37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  1   5.40% the\n",
      "  2  10.42% ,\n",
      "  3  14.67% .\n",
      "  4  17.78% of\n",
      "  5  20.19% and\n",
      "  6  22.40% to\n",
      "  7  24.29% a\n",
      "  8  25.97% in\n"
     ]
    }
   ],
   "source": [
    "fd = nltk.FreqDist(nltk.corpus.brown.words())\n",
    "cumulative = 0.0\n",
    "most_common_words = [word for (word, count) in fd.most_common()]\n",
    "for rank, word in enumerate(most_common_words):\n",
    "    cumulative += fd.freq(word)\n",
    "    print(\"%3d %6.2f%% %s\" % (rank + 1, cumulative * 100, word))\n",
    "    if cumulative > 0.25:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f304a6",
   "metadata": {},
   "source": [
    "到目前为止，使用循环变量存储最大值或最小值，有时很诱人。让我们用这种方法找出文本中最长的词。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3842c03a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'unextinguishable'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = nltk.corpus.gutenberg.words('milton-paradise.txt')\n",
    "longest = ''\n",
    "for word in text:\n",
    "    if len(word) > len(longest):\n",
    "        longest = word\n",
    "longest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9732b96",
   "metadata": {},
   "source": [
    "然而，一个更加清楚的解决方案是使用两个列表推导，它们的形式现在应该很熟悉："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "950c0d89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['unextinguishable',\n",
       " 'transubstantiate',\n",
       " 'inextinguishable',\n",
       " 'incomprehensible']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxlen = max(len(word) for word in text)\n",
    "[word for word in text if len(word) == maxlen]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "347d743b",
   "metadata": {},
   "source": [
    "请注意，我们的第一个解决方案找到第一个长度最长的词，而第二种方案找到*所有*最长的词（通常是我们想要的）。虽然有两个解决方案之间的理论效率的差异，主要的开销是到内存中读取数据；一旦数据准备好，第二阶段处理数据可以瞬间高效完成。我们还需要平衡我们对程序的效率与程序员的效率的关注。一种快速但神秘的解决方案将是更难理解和维护的。\n",
    "\n",
    "### 计数器的一些合理用途\n",
    "\n",
    "在有些情况下，我们仍然要在列表推导中使用循环变量。例如：我们需要使用一个循环变量中提取列表中连续重叠的n-grams："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a761962c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['The', 'dog', 'gave'],\n",
       " ['dog', 'gave', 'John'],\n",
       " ['gave', 'John', 'the'],\n",
       " ['John', 'the', 'newspaper']]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent = ['The', 'dog', 'gave', 'John', 'the', 'newspaper']\n",
    "n = 3\n",
    "[sent[i:i+n] for i in range(len(sent)-n+1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27eefb95",
   "metadata": {},
   "source": [
    "确保循环变量范围的正确相当棘手的。因为这是NLP中的常见操作，NLTK提供了支持函数`bigrams(text)`、`trigrams(text)`和一个更通用的`ngrams(text, n)`。\n",
    "\n",
    "下面是我们如何使用循环变量构建多维结构的一个例子。例如，建立一个*m*行*n*列的数组，其中每个元素是一个集合，我们可以使用一个嵌套的列表推导："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f2b4d176",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[set(), set(), set(), set(), set(), set(), set()],\n",
      " [set(), set(), set(), set(), set(), set(), set()],\n",
      " [set(), set(), set(), set(), set(), {'Alice'}, set()]]\n"
     ]
    }
   ],
   "source": [
    "import pprint # 导入 pprint 库，美化输出\n",
    "\n",
    "m, n = 3, 7\n",
    "array = [[set() for i in range(n)] for j in range(m)]\n",
    "array[2][5].add('Alice')\n",
    "pprint.pprint(array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d33c992",
   "metadata": {},
   "source": [
    "请看循环变量`i`和`j`在产生对象过程中没有用到，它们只是需要一个语法正确的`for` 语句。这种用法的另一个例子，请看表达式`['very' for i in range(3)]`产生一个包含三个`'very'`实例的列表，没有整数。\n",
    "\n",
    "请注意，由于我们前面所讨论的有关对象复制的原因，使用乘法做这项工作是不正确的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1a0bd5bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[{7}, {7}, {7}, {7}, {7}, {7}, {7}],\n",
      " [{7}, {7}, {7}, {7}, {7}, {7}, {7}],\n",
      " [{7}, {7}, {7}, {7}, {7}, {7}, {7}]]\n"
     ]
    }
   ],
   "source": [
    "array = [[set()] * n] * m\n",
    "array[2][5].add(7)\n",
    "pprint.pprint(array)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "61037b08",
   "metadata": {},
   "source": [
    "迭代是一个重要的编程概念。采取其他语言中的习惯用法是很诱人的。然而， Python提供一些优雅和高度可读的替代品，正如我们已经看到。\n",
    "\n",
    "## 4.4 函数 结构化编程的基础\n",
    "\n",
    "<a href=\"#参数传递\">1. 参数传递</a>\n",
    "\n",
    "<a href=\"#变量的作用域\">2. 变量的作用域</a>\n",
    "\n",
    "<a href=\"#参数类型检查\">3. 参数类型检查</a>\n",
    "\n",
    "<a href=\"#功能分解\">4. 功能分解</a>\n",
    "\n",
    "<a href=\"#编写函数的文档\">5. 编写函数的文档</a>\n",
    "\n",
    "函数提供了程序代码打包和重用的有效途径，已经在[3](https://usyiyi.github.io/nlp-py-2e-zh/ch02.html#sec-reusing-code)中解释过。例如，假设我们发现我们经常要从HTML文件读取文本。这包括以下几个步骤，打开文件，将它读入，规范化空白符号，剥离HTML标记。我们可以将这些步骤收集到一个函数中，并给它一个名字，如`get_text()`，如[4.2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-get-text)所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "940d2a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def get_text(file):\n",
    "    \"\"\"Read text from a file, normalizing whitespace and stripping HTML markup.\"\"\"\n",
    "    text = open(file).read()\n",
    "    text = re.sub(r'<.*?>', ' ', text)\n",
    "    text = re.sub('\\s+', ' ', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9093c7",
   "metadata": {},
   "source": [
    "现在，任何时候我们想从一个HTML文件得到干净的文字，都可以用文件的名字作为唯一的参数调用`get_text()`。它会返回一个字符串，我们可以将它指定给一个变量，例如：`contents = get_text(\"test.html\")`。每次我们要使用这一系列的步骤，只需要调用这个函数。\n",
    "\n",
    "使用函数可以为我们的程序节约空间。更重要的是，我们为函数选择名称可以提高程序*可读性*。在上面的例子中，只要我们的程序需要从文件读取干净的文本，我们不必弄乱这四行代码的程序，只需要调用`get_text()`。这种命名方式有助于提供一些“语义解释”——它可以帮助我们的程序的读者理解程序的“意思”。\n",
    "\n",
    "请注意，上面的函数定义包含一个字符串。函数定义内的第一个字符串被称为文档字符串。它不仅为阅读代码的人记录函数的功能，从文件加载这段代码的程序员也能够访问："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d646f991",
   "metadata": {},
   "outputs": [],
   "source": [
    "help(get_text)\n",
    "Help on function get_text in module __main__:\n",
    "\n",
    "get(text)\n",
    "    Read text from a file, normalizing whitespace and stripping HTML markup."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "37f3b690",
   "metadata": {},
   "source": [
    "我们首先定义函数的两个参数，`msg`和`num`  [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#fun-def)。然后调用函数，并传递给它两个参数，`monty`和`3`  [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#fun-call)；这些参数填补了参数提供的“占位符”，为函数体中出现的`msg`和`num`提供值。\n",
    "\n",
    "我们看到在下面的例子中不需要有任何参数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2f70fda9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Monty Python'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def monty():\n",
    "    return \"Monty Python\"\n",
    "monty()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "124ef652",
   "metadata": {},
   "source": [
    "函数通常会通过`return`语句将其结果返回给调用它的程序，正如我们刚才看到的。对于调用程序，它看起来就像函数调用已被函数结果替代，例如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cfa999ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "repeat('Monty Python', 3)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import repeat\n",
    "\n",
    "repeat(monty(), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "df283c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "repeat('Monty Python', 3)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repeat('Monty Python', 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495ad445",
   "metadata": {},
   "source": [
    "一个Python函数并不是一定需要有一个return语句。有些函数做它们的工作的同时会附带输出结果、修改文件或者更新参数的内容。（这种函数在其他一些编程语言中被称为“过程”）。\n",
    "\n",
    "考虑以下三个排序函数。第三个是危险的，因为程序员可能没有意识到它已经修改了给它的输入。一般情况下，函数应该修改参数的内容（`my_sort1()`）或返回一个值（`my_sort2()`），而不是两个都做（`my_sort3()`）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2a725f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_sort1(mylist):      # good: modifies its argument, no return value\n",
    "    mylist.sort()\n",
    "def my_sort2(mylist):      # good: doesn't touch its argument, returns value\n",
    "    return sorted(mylist)\n",
    "def my_sort3(mylist):      # bad: modifies its argument and also returns it\n",
    "    mylist.sort()\n",
    "    return mylist"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92dfe14c",
   "metadata": {},
   "source": [
    "### 参数传递\n",
    "\n",
    "早在[4.1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#sec-back-to-the-basics)节中，你就已经看到了赋值操作，而一个结构化对象的值是该对象的引用。函数也是一样的。Python按它的值来解释函数的参数（这被称为按值调用）。在下面的代码中，`set_up()`有两个参数，都在函数内部被修改。我们一开始将一个空字符串分配给`w`，将一个空列表分配给`p`。调用该函数后，`w`没有变，而`p`改变了："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "848154b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def set_up(word, properties):\n",
    "    word = 'lolcat'\n",
    "    properties.append('noun')\n",
    "    properties = 5\n",
    "\n",
    "w = ''\n",
    "p = []\n",
    "set_up(w, p)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f69036a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['noun']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc17a98a",
   "metadata": {},
   "source": [
    "请注意，`w`没有被函数改变。当我们调用`set_up(w, p)`时，`w`（空字符串）的值被分配到一个新的变量`word`。在函数内部`word`值被修改。然而，这种变化并没有传播给`w`。这个参数传递过程与下面的赋值序列是一样的："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f5a004e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = ''\n",
    "word = w\n",
    "word = 'lolcat'\n",
    "w"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac84769",
   "metadata": {},
   "source": [
    "让我们来看看列表`p`上发生了什么。当我们调用`set_up(w, p)`，`p`的值（一个空列表的引用）被分配到一个新的本地变量`properties`，所以现在这两个变量引用相同的内存位置。函数修改`properties`，而这种变化也反映在`p`值上，正如我们所看到的。函数也分配给properties一个新的值（数字`5`）；这并不能修改该内存位置上的内容，而是创建了一个新的局部变量。这种行为就好像是我们做了下列赋值序列："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "94eedc1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['noun']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = []\n",
    "properties = p\n",
    "properties.append('noun')\n",
    "properties = 5\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e121e25",
   "metadata": {},
   "source": [
    "因此，要理解Python按值传递参数，只要了解它是如何赋值的就足够了。记住，你可以使用`id()`函数和`is`操作符来检查每个语句执行之后你对对象标识符的理解。\n",
    "\n",
    "### 变量的作用域\n",
    "\n",
    "函数定义为变量创建了一个新的局部的作用域。当你在函数体内部分配一个新的变量时，这个名字只在该函数内部被定义。函数体外或者在其它函数体内，这个名字是不可见的。这一行为意味着你可以选择变量名而不必担心它与你的其他函数定义中使用的名称冲突。\n",
    "\n",
    "当你在一个函数体内部使用一个现有的名字时，Python解释器先尝试按照函数本地的名字来解释。如果没有发现，解释器检查它是否是一个模块内的全局名称。最后，如果没有成功，解释器会检查是否是Python内置的名字。这就是所谓的名称解析的LGB规则：本地（local），全局（global），然后内置（built-in）。\n",
    "\n",
    "小心！\n",
    "\n",
    "一个函数可以使用`global`声明创建一个新的全局变量。然而，这种做法应尽可能避免。在函数内部定义全局变量会导致上下文依赖性而限制函数的可移植性（或重用性）。一般来说，你应该使用参数作为函数的输入，返回值作为函数的输出。\n",
    "\n",
    "### 参数类型检查\n",
    "\n",
    "我们写程序时，Python不会强迫我们声明变量的类型，这允许我们定义参数类型灵活的函数。例如，我们可能希望一个标注只是一个词序列，而不管这个序列被表示为一个列表、元组（或是迭代器，一种新的序列类型，超出了当前的讨论范围）。\n",
    "\n",
    "然而，我们常常想写一些能被他人利用的程序，并希望以一种防守的风格，当函数没有被正确调用时提供有益的警告。下面的`tag()`函数的作者假设其参数将始终是一个字符串。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3ed45e74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'det'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def tag(word):\n",
    "    if word in ['a', 'the', 'all']:\n",
    "        return 'det'\n",
    "    else:\n",
    "        return 'noun'\n",
    "\n",
    "tag('the')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3da12740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'noun'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag('knight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e2357b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'noun'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag([\"'Tis\", 'but', 'a', 'scratch']) # 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a1ca7b40",
   "metadata": {},
   "source": [
    "该函数对参数`'the'`和`'knight'`返回合理的值，传递给它一个列表 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#list-arg)，看看会发生什么——它没有抱怨，虽然它返回的结果显然是不正确的。此函数的作者可以采取一些额外的步骤来确保`tag()`函数的参数`word`是一个字符串。一种直白的做法是使用`if not type(word) is str`检查参数的类型，如果`word`不是一个字符串，简单地返回Python特殊的空值`None`。这是一个略微的改善，因为该函数在检查参数类型，并试图对错误的输入返回一个“特殊的”诊断结果。然而，它也是危险的，因为调用程序可能不会检测`None`是故意设定的“特殊”值，这种诊断的返回值可能被传播到程序的其他部分产生不可预测的后果。如果这个词是一个Unicode字符串这种方法也会失败。因为它的类型是`unicode`而不是`str`。这里有一个更好的解决方案，使用`assert`语句和Python的`basestring`的类型一起，它是`unicode`和`str`的共同类型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6ac109de",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tag(word):\n",
    "    assert isinstance(word, basestring), \"argument to tag() must be a string\"\n",
    "    if word in ['a', 'the', 'all']:\n",
    "        return 'det'\n",
    "    else:\n",
    "        return 'noun'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee3f45d5",
   "metadata": {},
   "source": [
    "如果`assert`语句失败，它会产生一个不可忽视的错误而停止程序执行。此外，该错误信息是容易理解的。程序中添加断言能帮助你找到逻辑错误，是一种防御性编程。一个更根本的方法是在本节后面描述的使用文档字符串为每个函数记录参数的文档。\n",
    "\n",
    "### 功能分解\n",
    "\n",
    "结构良好的程序通常都广泛使用函数。当一个程序代码块增长到超过10-20行，如果将代码分成一个或多个函数，每一个有明确的目的，这将对可读性有很大的帮助。这类似于好文章被划分成段，每段话表示一个主要思想。\n",
    "\n",
    "函数提供了一种重要的抽象。它们让我们将多个动作组合成一个单一的复杂的行动，并给它关联一个名称。（比较我们组合动作go和bring back为一个单一的更复杂的动作fetch。）当我们使用函数时，主程序可以在一个更高的抽象水平编写，使其结构更透明，例如"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c7c12da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from flair.data_fetcher import NLPTaskDataFetcher # flair 是一个 NLP 框架 pip install flair==0.5 \n",
    "\n",
    "data = load_corpus()\n",
    "results = analyze(data)\n",
    "present(results)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "606f5522",
   "metadata": {},
   "source": [
    "适当使用函数使程序更具可读性和可维护性。另外，重新实现一个函数已成为可能——使用更高效的代码替换函数体——不需要关心程序的其余部分。\n",
    "\n",
    "思考[4.3](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-freq-words1)中`freq_words`函数。它更新一个作为参数传递进来的频率分布的内容，并输出前n个最频繁的词的列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1198ea2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib import request\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "def freq_words(url, freqdist, n):\n",
    "    html = request.urlopen(url).read().decode('utf8')\n",
    "    raw = BeautifulSoup(html).get_text()\n",
    "    for word in word_tokenize(raw):\n",
    "        freqdist[word.lower()] += 1\n",
    "    result = []\n",
    "    for word, count in freqdist.most_common(n):\n",
    "        result = result + [word]\n",
    "    print(result)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2786be90",
   "metadata": {},
   "source": [
    "这个函数有几个问题。该函数有两个副作用：它修改了第二个参数的内容，并输出它已计算的结果的经过选择的子集。如果我们在函数内部初始化`FreqDist()`对象（在它被处理的同一个地方），并且去掉选择集而将结果显示给调用程序的话，函数会更容易理解和更容易在其他地方重用。考虑到它的任务是找出频繁的一个词，它应该只应该返回一个列表，而不是整个频率分布。在[4.4](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-freq-words2)中，我们重构此函数，并通过去掉`freqdist`参数简化其接口。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "31605648",
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib import request\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "def freq_words(url, n):\n",
    "    html = request.urlopen(url).read().decode('utf8')\n",
    "    text = BeautifulSoup(html).get_text()\n",
    "    freqdist = nltk.FreqDist(word.lower() for word in word_tokenize(text))\n",
    "    return [word for (word, _) in fd.most_common(n)]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "71ad74b5",
   "metadata": {},
   "source": [
    "`freq_words`函数的可读性和可用性得到改进。\n",
    "\n",
    "注意\n",
    "\n",
    "我们将`_`用作变量名。这是对任何其他变量没有什么不同，除了它向读者发出信号，我们没有使用它保存的信息。\n",
    "\n",
    "### 编写函数的文档\n",
    "\n",
    "如果我们已经将工作分解成函数分解的很好了，那么应该很容易使用通俗易懂的语言描述每个函数的目的，并且在函数的定义顶部的文档字符串中提供这些描述。这个说明不应该解释函数是如何实现的；实际上，应该能够不改变这个说明，使用不同的方法，重新实现这个函数。\n",
    "\n",
    "对于最简单的函数，一个单行的文档字符串通常就足够了（见[4.2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-get-text)）。你应该提供一个在一行中包含一个完整的句子的三重引号引起来的字符串。对于不寻常的函数，你还是应该在第一行提供一个一句话总结，因为很多的文档字符串处理工具会索引这个字符串。它后面应该有一个空行，然后是更详细的功能说明（见`http://www.python.org/dev/peps/pep-0257/`的文档字符串约定的更多信息）。\n",
    "\n",
    "文档字符串可以包括一个doctest块，说明使用的函数和预期的输出。这些都可以使用Python的`docutils`模块自动测试。文档字符串应当记录函数的每个参数的类型和返回类型。至少可以用纯文本来做这些。然而，请注意，NLTK使用Sphinx标记语言来记录参数。这种格式可以自动转换成富结构化的API文档（见`http://nltk.org/`），并包含某些“字段”的特殊处理，例如`param`，允许清楚地记录函数的输入和输出。[4.5](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-sphinx)演示了一个完整的文档字符串。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4cef7de9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(reference, test):\n",
    "    \"\"\"\n",
    "    Calculate the fraction of test items that equal the corresponding reference items.\n",
    "\n",
    "    Given a list of reference values and a corresponding list of test values,\n",
    "    return the fraction of corresponding values that are equal.\n",
    "    In particular, return the fraction of indexes\n",
    "    {0<i<=len(test)} such that C{test[i] == reference[i]}.\n",
    "\n",
    "        accuracy(['ADJ', 'N', 'V', 'N'], ['N', 'N', 'V', 'ADJ'])\n",
    "        0.5\n",
    "\n",
    "    :param reference: An ordered list of reference values\n",
    "    :type reference: list\n",
    "    :param test: A list of values to compare against the corresponding\n",
    "        reference values\n",
    "    :type test: list\n",
    "    :return: the accuracy score\n",
    "    :rtype: float\n",
    "    :raises ValueError: If reference and length do not have the same length\n",
    "    \"\"\"\n",
    "\n",
    "    if len(reference) != len(test):\n",
    "        raise ValueError(\"Lists must have the same length.\")\n",
    "    num_correct = 0\n",
    "    for x, y in zip(reference, test):\n",
    "        if x == y:\n",
    "            num_correct += 1\n",
    "    return float(num_correct) / len(reference)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a50fb248",
   "metadata": {},
   "source": [
    "## 4.5 更多关于函数\n",
    "\n",
    "本节将讨论更高级的特性，你在第一次阅读本章时可能更愿意跳过此节。\n",
    "\n",
    "<a href=\"#作为参数的函数\">1. 作为参数的函数</a>\n",
    "\n",
    "<a href=\"#累计函数\">2. 累计函数</a>\n",
    "\n",
    "<a href=\"#高阶函数\">3. 高阶函数</a>\n",
    "\n",
    "<a href=\"#命名的参数\">4. 命名的参数</a>\n",
    "\n",
    "### 作为参数的函数\n",
    "\n",
    "到目前为止，我们传递给函数的参数一直都是简单的对象，如字符串或列表等结构化对象。Python也允许我们传递一个函数作为另一个函数的参数。现在，我们可以抽象出操作，对相同数据进行不同操作。正如下面的例子表示的，我们可以传递内置函数`len()`或用户定义的函数`last_letter()`作为另一个函数的参数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "90312ba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['e', 'e', 'f', 'e', 'e', ',', 'd', 'e', 's', 'l', 'e', 'e', 'f', 's', '.']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent = ['Take', 'care', 'of', 'the', 'sense', ',', 'and', 'the',\n",
    "        'sounds', 'will', 'take', 'care', 'of', 'themselves', '.']\n",
    "def extract_property(prop):\n",
    "    return [prop(word) for word in sent]\n",
    "\n",
    "extract_property(len)\n",
    "[4, 4, 2, 3, 5, 1, 3, 3, 6, 4, 4, 4, 2, 10, 1]\n",
    "def last_letter(word):\n",
    "    return word[-1]\n",
    "extract_property(last_letter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ccb3cb",
   "metadata": {},
   "source": [
    "对象`len`和`last_letter`可以像列表和字典那样被传递。请注意，只有在我们调用该函数时，才在函数名后使用括号；当我们只是将函数作为一个对象，括号被省略。\n",
    "\n",
    "Python提供了更多的方式来定义函数作为其他函数的参数，即所谓的lambda 表达式。试想在很多地方没有必要使用上述的`last_letter()`函数，因此没有必要给它一个名字。我们可以等价地写以下内容："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "91df406b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['e', 'e', 'f', 'e', 'e', ',', 'd', 'e', 's', 'l', 'e', 'e', 'f', 's', '.']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_property(lambda w: w[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84947d55",
   "metadata": {},
   "source": [
    "我们的下一个例子演示传递一个函数给`sorted()`函数。当我们用唯一的参数（需要排序的链表）调用后者，它使用内置的比较函数`cmp()`。然而，我们可以提供自己的排序函数，例如按长度递减排序。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "fadfe03e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[',',\n",
       " '.',\n",
       " 'Take',\n",
       " 'and',\n",
       " 'care',\n",
       " 'care',\n",
       " 'of',\n",
       " 'of',\n",
       " 'sense',\n",
       " 'sounds',\n",
       " 'take',\n",
       " 'the',\n",
       " 'the',\n",
       " 'themselves',\n",
       " 'will']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80811ba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted(sent, cmp) # cmp() 是 python 2 的函数，python 3 废弃了，有兴趣可查询了解 python 3 的 richcmp 方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c2efa93",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted(sent, lambda x, y: cmp(len(y), len(x)))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a3b7f17b",
   "metadata": {},
   "source": [
    "### 累计函数\n",
    "\n",
    "这些函数以初始化一些存储开始，迭代和处理输入的数据，最后返回一些最终的对象（一个大的结构或汇总的结果）。做到这一点的一个标准的方式是初始化一个空链表，累计材料，然后返回这个链表，如[4.6](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-search-examples)中所示函数`search1()`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c84f6c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def search1(substring, words):\n",
    "    result = []\n",
    "    for word in words:\n",
    "        if substring in word:\n",
    "            result.append(word)\n",
    "    return result\n",
    "\n",
    "def search2(substring, words):\n",
    "    for word in words:\n",
    "        if substring in word:\n",
    "            yield word"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ec794142",
   "metadata": {},
   "source": [
    "函数`search2()`是一个生成器。第一次调用此函数，它运行到`yield`语句然后停下来。调用程序获得第一个词，完成任何必要的处理。一旦调用程序对另一个词做好准备，函数会从停下来的地方继续执行，直到再次遇到`yield`语句。这种方法通常更有效，因为函数只产生调用程序需要的数据，并不需要分配额外的内存来存储输出（参见前面关于生成器表达式的讨论）。\n",
    "\n",
    "下面是一个更复杂的生成器的例子，产生一个词列表的所有排列。为了强制`permutations()`函数产生所有它的输出，我们将它包装在`list()`调用中 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#listperm)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "cfcf7c76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['police', 'fish', 'buffalo'],\n",
       " ['fish', 'police', 'buffalo'],\n",
       " ['fish', 'buffalo', 'police'],\n",
       " ['police', 'buffalo', 'fish'],\n",
       " ['buffalo', 'police', 'fish'],\n",
       " ['buffalo', 'fish', 'police']]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def permutations(seq):\n",
    "    if len(seq) <= 1:\n",
    "        yield seq\n",
    "    else:\n",
    "        for perm in permutations(seq[1:]):\n",
    "            for i in range(len(perm)+1):\n",
    "                yield perm[:i] + seq[0:1] + perm[i:]\n",
    "\n",
    "list(permutations(['police', 'fish', 'buffalo'])) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "feb62ec6",
   "metadata": {},
   "source": [
    "注意\n",
    "\n",
    "`permutations`函数使用了一种技术叫递归，将在下面[4.7](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#sec-algorithm-design)讨论。产生一组词的排列对于创建测试一个语法的数据十分有用（[8.](https://usyiyi.github.io/nlp-py-2e-zh/ch08.html#chap-parse))。\n",
    "\n",
    "### 高阶函数\n",
    "\n",
    "Python提供一些具有函数式编程语言如Haskell标准特征的高阶函数。我们将在这里演示它们，与使用列表推导的相对应的表达一起。\n",
    "\n",
    "让我们从定义一个函数`is_content_word()`开始，它检查一个词是否来自一个开放的实词类。我们使用此函数作为`filter()`的第一个参数，它对作为它的第二个参数的序列中的每个项目运用该函数，只保留该函数返回`True`的项目。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e2876128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Take', 'care', 'sense', 'sounds', 'take', 'care', 'themselves']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_content_word(word):\n",
    "    return word.lower() not in ['a', 'of', 'the', 'and', 'will', ',', '.']\n",
    "sent = ['Take', 'care', 'of', 'the', 'sense', ',', 'and', 'the',\n",
    "        'sounds', 'will', 'take', 'care', 'of', 'themselves', '.']\n",
    "list(filter(is_content_word, sent))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "32584b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Take', 'care', 'sense', 'sounds', 'take', 'care', 'themselves']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[w for w in sent if is_content_word(w)]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7b2dc865",
   "metadata": {},
   "source": [
    "另一个高阶函数是`map()`，将一个函数运用到一个序列中的每一项。它是我们在[4.5](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#sec-doing-more-with-functions)看到的函数`extract_property()`的一个通用版本。这里是一个简单的方法找出布朗语料库新闻部分中的句子的平均长度，后面跟着的是使用列表推导计算的等效版本："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c7ddeabe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.75081116158339"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lengths = list(map(len, nltk.corpus.brown.sents(categories='news')))\n",
    "sum(lengths) / len(lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3bee7b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.75081116158339"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lengths = [len(sent) for sent in nltk.corpus.brown.sents(categories='news')]\n",
    "sum(lengths) / len(lengths)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bbcae0c",
   "metadata": {},
   "source": [
    "在上面的例子中，我们指定了一个用户定义的函数`is_content_word()` 和一个内置函数`len()`。我们还可以提供一个lambda 表达式。这里是两个等效的例子，计数每个词中的元音的数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28414511",
   "metadata": {},
   "outputs": [],
   "source": [
    "list(map(lambda w: len(filter(lambda c: c.lower() in \"aeiou\", w)), sent)) # filter 对象不支持 len() 操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62a42135",
   "metadata": {},
   "outputs": [],
   "source": [
    "[len(c for c in w if c.lower() in \"aeiou\") for w in sent] # generator 对象不支持 len() 操作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdde8cf6",
   "metadata": {},
   "source": [
    "列表推导为基础的解决方案通常比基于高阶函数的解决方案可读性更好，我们在整个这本书的青睐于使用前者。\n",
    "\n",
    "### 命名的参数\n",
    "\n",
    "当有很多参数时，很容易混淆正确的顺序。我们可以通过名字引用参数，甚至可以给它们分配默认值以供调用程序没有提供该参数时使用。现在参数可以按任意顺序指定，也可以省略。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "dd258941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<empty><empty><empty>'"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def repeat(msg='<empty>', num=1):\n",
    "    return msg * num\n",
    "repeat(num=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "19d0a1e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Alice'"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repeat(msg='Alice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "86dff4ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AliceAliceAliceAliceAlice'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repeat(num=5, msg='Alice')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3f3acd33",
   "metadata": {},
   "source": [
    "这些被称为关键字参数。如果我们混合使用这两种参数，就必须确保未命名的参数在命名的参数前面。必须是这样，因为未命名参数是根据位置来定义的。我们可以定义一个函数，接受任意数量的未命名和命名参数，并通过一个就地的参数列表`*args`和一个就地的关键字参数字典`**kwargs`来访问它们。（字典将在[3](https://usyiyi.github.io/nlp-py-2e-zh/ch05.html#sec-dictionaries)中讲述。）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "cfa119be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 'African swallow')\n",
      "{'monty': 'python'}\n"
     ]
    }
   ],
   "source": [
    "def generic(*args, **kwargs):\n",
    "    print(args)\n",
    "    print(kwargs)\n",
    "\n",
    "generic(1, \"African swallow\", monty=\"python\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2749182b",
   "metadata": {},
   "source": [
    "当`*args`作为函数参数时，它实际上对应函数所有的未命名参数。下面是另一个这方面的Python语法的演示，处理可变数目的参数的函数`zip()`。我们将使用变量名`*song`来表示名字`*args`并没有什么特别的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5590b913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('four', 'three', 'two'),\n",
       " ('calling', 'French', 'turtle'),\n",
       " ('birds', 'hens', 'doves')]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "song = [['four', 'calling', 'birds'],\n",
    "        ['three', 'French', 'hens'],\n",
    "        ['two', 'turtle', 'doves']]\n",
    "list(zip(song[0], song[1], song[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "56239e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('four', 'three', 'two'),\n",
       " ('calling', 'French', 'turtle'),\n",
       " ('birds', 'hens', 'doves')]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(*song))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1078a731",
   "metadata": {},
   "source": [
    "应该从这个例子中明白输入`*song`仅仅是一个方便的记号，相当于输入了`song[0], song[1], song[2]`。\n",
    "\n",
    "下面是另一个在函数的定义中使用关键字参数的例子，有三种等效的方法来调用这个函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8febf459",
   "metadata": {},
   "outputs": [],
   "source": [
    "def freq_words(file, min=1, num=10):\n",
    "    text = open(file).read()\n",
    "    tokens = word_tokenize(text)\n",
    "    freqdist = nltk.FreqDist(t for t in tokens if len(t) >= min)\n",
    "    return freqdist.most_common(num)\n",
    "fw = freq_words('ch01.rst', 4, 10) # 全文没提到过 .rst 文件的来源\n",
    "fw = freq_words('ch01.rst', min=4, num=10)\n",
    "fw = freq_words('ch01.rst', num=10, min=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fa4854",
   "metadata": {},
   "source": [
    "命名参数的另一个作用是它们允许选择性使用参数。因此，我们可以在我们高兴使用默认值的地方省略任何参数：`freq_words('ch01.rst', min=4)`, `freq_words('ch01.rst', 4)`。可选参数的另一个常见用途是作为标志使用。这里是同一个的函数的修订版本，如果设置了`verbose`标志将会报告其进展情况："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "092dd675",
   "metadata": {},
   "outputs": [],
   "source": [
    "def freq_words(file, min=1, num=10, verbose=False):\n",
    "    freqdist = FreqDist()\n",
    "    if verbose: print(\"Opening\", file)\n",
    "    text = open(file).read()\n",
    "    if verbose: print(\"Read in %d characters\" % len(file))\n",
    "    for word in word_tokenize(text):\n",
    "        if len(word) >= min:\n",
    "            freqdist[word] += 1\n",
    "            if verbose and freqdist.N() % 100 == 0: print(\".\", sep=\"\")\n",
    "    if verbose: print\n",
    "    return freqdist.most_common(num)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bc51a50",
   "metadata": {},
   "source": [
    "小心！\n",
    "\n",
    "注意不要使用可变对象作为参数的默认值。这个函数的一系列调用将使用同一个对象，有时会出现离奇的结果，就像我们稍后会在关于调试的讨论中看到的那样。\n",
    "\n",
    "小心！\n",
    "\n",
    "如果你的程序将使用大量的文件，它是一个好主意来关闭任何一旦不再需要的已经打开的文件。如果你使用`with`语句，Python会自动关闭打开的文件︰"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3311560d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"lexicon.txt\") as f: # 又一个没有来源的例子\n",
    "    data = f.read()\n",
    "    # process the data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "75f295b2",
   "metadata": {},
   "source": [
    "## 4.6 程序开发\n",
    "\n",
    "编程是一种技能，需要获得几年的各种编程语言和任务的经验。关键的高层次能力是*算法设计*及其在*结构化编程*中的实现。关键的低层次的能力包括熟悉语言的语法结构，以及排除故障的程序（不能表现预期的行为的程序）的各种诊断方法的知识。\n",
    "\n",
    "本节描述一个程序模块的内部结构，以及如何组织一个多模块的程序。然后描述程序开发过程中出现的各种错误，你可以做些什么来解决这些问题，更好的是，从一开始就避免它们。\n",
    "\n",
    "<a href=\"#python模块的结构\">1. Python模块的结构</a>\n",
    "\n",
    "<a href=\"#多模块程序\">2. 多模块程序</a>\n",
    "\n",
    "<a href=\"#错误源头\">3. 错误源头</a>\n",
    "\n",
    "<a href=\"#调试技术\">4. 调试技术</a>\n",
    "\n",
    "<a href=\"#防御性编程\">5. 防御性编程</a>\n",
    "\n",
    "### Python模块的结构\n",
    "\n",
    "程序模块的目的是把逻辑上相关的定义和函数结合在一起，以方便重用和更高层次的抽象。Python 模块只是一些单独的`.py`文件。例如，如果你在处理一种特定的语料格式，读取和写入这种格式的函数可以放在一起。这两种格式所使用的常量，如字段分隔符或一个`EXTN = \".inf\"`文件扩展名，可以共享。如果要更新格式，你就会知道只有一个文件需要改变。类似地，一个模块可以包含用于创建和操纵一种特定的数据结构如语法树的代码，或执行特定的处理任务如绘制语料统计图表的代码。\n",
    "\n",
    "当你开始编写Python模块，有一些例子来模拟是有益的。你可以使用变量`__file__`定位你的系统中任一NLTK模块的代码，例如："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a40b5bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "nltk.metrics.distance.__file__\n",
    "'/usr/lib/python2.5/site-packages/nltk/metrics/distance.pyc'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf95f60",
   "metadata": {},
   "source": [
    "这将返回模块已编译`.pyc`文件的位置，在你的机器上你可能看到的位置不同。你需要打开的文件是对应的`.py`源文件，它和`.pyc`文件放在同一目录下。另外，你可以在网站上查看该模块的最新版本`http://code.google.com/p/nltk/source/browse/trunk/nltk/nltk/metrics/distance.py`。\n",
    "\n",
    "与其他NLTK的模块一样，`distance.py`以一组注释行开始，包括一行模块标题和作者信息。（由于代码会被发布，也包括代码可用的URL、版权声明和许可信息。）接下来是模块级的文档字符串，三重引号的多行字符串，其中包括当有人输入`help(nltk.metrics.distance)`将被输出的关于模块的信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd29bc4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Natural Language Toolkit: Distance Metrics\n",
    "#\n",
    "# Copyright (C) 2001-2013 NLTK Project\n",
    "# Author: Edward Loper <edloper@gmail.com>\n",
    "#         Steven Bird <stevenbird1@gmail.com>\n",
    "#         Tom Lippincott <tom@cs.columbia.edu>\n",
    "# URL: <http://nltk.org/>\n",
    "# For license information, see LICENSE.TXT\n",
    "#\n",
    "\n",
    "\"\"\"\n",
    "Distance Metrics.\n",
    "\n",
    "Compute the distance between two items (usually strings).\n",
    "As metrics, they must satisfy the following three requirements:\n",
    "\n",
    "1. d(a, a) = 0\n",
    "2. d(a, b) >= 0\n",
    "3. d(a, c) <= d(a, b) + d(b, c)\n",
    "\"\"\""
   ]
  },
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c9df4782",
   "metadata": {},
   "source": [
    "### 多模块程序\n",
    "\n",
    "一些程序汇集多种任务，例如从语料库加载数据、对数据进行一些分析、然后将其可视化。我们可能已经有了稳定的模块来加载数据和实现数据可视化。我们的工作可能会涉及到那些分析任务的编码，只是从现有的模块调用一些函数。[4.7](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#fig-multi-module)描述了这种情景。\n",
    "\n",
    "![e685801a8cec4515b47e1bda95deb59d.png](attachment:e685801a8cec4515b47e1bda95deb59d.png)\n",
    "\n",
    "图 4.7：一个多模块程序的结构：主程序`my_program.py`从其他两个模块导入函数；独特的分析任务在主程序本地进行，而一般的载入和可视化任务被分离开以便可以重用和抽象。\n",
    "\n",
    "通过将我们的工作分成几个模块和使用`import`语句访问别处定义的函数，我们可以保持各个模块简单，易于维护。这种做法也将导致越来越多的模块的集合，使我们有可能建立复杂的涉及模块间层次结构的系统。设计这样的系统是一个复杂的软件工程任务，这超出了本书的范围。\n",
    "\n",
    "### 错误源头\n",
    "\n",
    "掌握编程技术取决于当程序不按预期运作时各种解决问题的技能的总结。一些琐碎的东西，如放错位置的符号，可能导致程序的行为异常。我们把这些叫做“bugs”，因为它们与它们所导致的损害相比较小。它们不知不觉的潜入我们的代码，只有在很久以后，我们在一些新的数据上运行程序时才会发现它们的存在。有时，一个错误的修复仅仅是暴露出另一个，于是我们得到了鲜明的印象，bug 在移动。我们唯一的安慰是bugs是自发的而不是程序员的错误。\n",
    "\n",
    "繁琐浮躁不谈，调试代码是很难的，因为有那么多的方式出现故障。我们对输入数据、算法甚至编程语言的理解可能是错误的。让我们分别来看看每种情况的例子。\n",
    "\n",
    "首先，输入的数据可能包含一些意想不到的字符。例如，WordNet的同义词集名称的形式是`tree.n.01`，由句号分割成3个部分。最初NLTK的WordNet模块使用`split('.')`分解这些名称。然而，当有人试图寻找词PhD时，这种方法就不能用了，它的同义集名称是`ph.d..n.01`，包含4个逗号而不是预期的2个。解决的办法是使用`rsplit('.', 2)`利用最右边的句号最多分割两次，留下字符串`ph.d.`不变。虽然在模块发布之前已经测试过，但就在几个星期前有人检测到这个问题（见`http://code.google.com/p/nltk/issues/detail?id=297`）。\n",
    "\n",
    "第二，提供的函数可能不会像预期的那样运作。例如，在测试NLTK中的WordNet接口时，一名作者注意到没有同义词集定义了反义词，而底层数据库提供了大量的反义词的信息。这看着像是WordNet接口中的一个错误，结果却是对WordNet本身的误解：反义词在词条中定义，而不是在义词集中。唯一的“bug”是对接口的一个误解（参见`http://code.google.com/p/nltk/issues/detail?id=98`）。\n",
    "\n",
    "第三，我们对Python语义的理解可能出错。很容易做出关于两个操作符的相对范围的错误的假设。例如，`\"%s.%s.%02d\" % \"ph.d.\", \"n\", 1`产生一个运行时错误`TypeError: not enough arguments for format string`。这是因为百分号操作符优先级高于逗号运算符。解决办法是添加括号强制限定所需的范围。作为另一个例子，假设我们定义一个函数来收集一个文本中给定长度的所有词符。该函数有文本和词长作为参数，还有一个额外的参数，允许指定结果的初始值作为参数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3d2f0de6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['omg', 'teh', 'teh', 'mat']"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_words(text, wordlength, result=[]):\n",
    "    for word in text:\n",
    "        if len(word) == wordlength:\n",
    "            result.append(word)\n",
    "    return result\n",
    "find_words(['omg', 'teh', 'lolcat', 'sitted', 'on', 'teh', 'mat'], 3) # 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a2f7cf6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ur', 'on']"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_words(['omg', 'teh', 'lolcat', 'sitted', 'on', 'teh', 'mat'], 2, ['ur']) # 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "fec33b74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['omg', 'teh', 'teh', 'mat', 'omg', 'teh', 'teh', 'mat']"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "find_words(['omg', 'teh', 'lolcat', 'sitted', 'on', 'teh', 'mat'], 3) # 3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7a27743c",
   "metadata": {},
   "source": [
    "我们第一次调用`find_words()` [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#find-words-1)，我们得到所有预期的三个字母的词。第二次，我们为result指定一个初始值，一个单元素列表`['ur']`，如预期，结果中有这个词连同我们的文本中的其他双字母的词。现在，我们再次使用 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#find-words-1)中相同的参数调用`find_words()` [# 3](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#find-words-3)，但我们得到了不同的结果！我们每次不使用第三个参数调用`find_words()`，结果都只会延长前次调用的结果，而不是以在函数定义中指定的空链表result开始。程序的行为并不如预期，因为我们错误地认为在函数被调用时会创建默认值。然而，它只创建了一次，在Python解释器加载这个函数时。这一个列表对象会被使用，只要没有给函数提供明确的值。\n",
    "\n",
    "### 调试技术\n",
    "\n",
    "由于大多数代码错误是因为程序员的不正确的假设，你检测bug要做的第一件事是检查你的假设。通过给程序添加`print`语句定位问题，显示重要的变量的值，并显示程序的进展程度。\n",
    "\n",
    "如果程序产生一个“异常”——运行时错误——解释器会输出一个堆栈跟踪，精确定位错误发生时程序执行的位置。如果程序取决于输入数据，尽量将它减少到能产生错误的最小尺寸。\n",
    "\n",
    "一旦你已经将问题定位在一个特定的函数或一行代码，你需要弄清楚是什么出了错误。使用交互式命令行重现错误发生时的情况往往是有益的。定义一些变量，然后复制粘贴可能出错的代码行到会话中，看看会发生什么。检查你对代码的理解，通过阅读一些文档和测试与你正在试图做的事情相同的其他代码示例。尝试将你的代码解释给别人听，也许他们会看出出错的地方。\n",
    "\n",
    "Python提供了一个调试器，它允许你监视程序的执行，指定程序暂停运行的行号（即断点），逐步调试代码段和检查变量的值。你可以如下方式在你的代码中调用调试器："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fded06c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pdb\n",
    "import mymodule\n",
    "pdb.run('mymodule.myfunction()')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e7b64b20",
   "metadata": {},
   "source": [
    "它会给出一个提示`(Pdb)`，你可以在那里输入指令给调试器。输入`help`来查看命令的完整列表。输入`step`(或只输入`s`)将执行当前行然后停止。如果当前行调用一个函数，它将进入这个函数并停止在第一行。输入`next`(或只输入`n`)是类似的，但它会在当前函数中的下一行停止执行。`break`（或`b`）命令可用于创建或列出断点。输入`continue`（或`c`）会继续执行直到遇到下一个断点。输入任何变量的名称可以检查它的值。\n",
    "\n",
    "我们可以使用Python调试器来查找`find_words()` 函数的问题。请记住问题是在第二次调用函数时产生的。我们一开始将不使用调试器而调用该函数 [_first-run_](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#id9)，使用可能的最小输入。第二次我们使用调试器调用它 [_second-run_](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#id11)。.. doctest-ignore:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "3c6dfc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['omg', 'teh', 'teh', 'mat', 'omg', 'teh', 'teh', 'mat', 'cat']"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pdb\n",
    "find_words(['cat'], 3) # [_first-run]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c91d508",
   "metadata": {},
   "outputs": [],
   "source": [
    "pdb.run(\"find_words(['dog'], 3)\") # [_second-run]"
   ]
  },
  {
   "attachments": {
    "1094084b61ac3f0e4416e92869c52ccd.png": {
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"
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   "cell_type": "markdown",
   "id": "a7f564d8",
   "metadata": {},
   "source": [
    "### 防御性编程\n",
    "\n",
    "为了避免一些调试的痛苦，养成防御性的编程习惯是有益的。不要写20行程序然后测试它，而是自下而上的打造一些明确可以运作的小的程序片。每次你将这些程序片组合成更大的单位都要仔细的看它是否能如预期的运作。考虑在你的代码中添加`assert`语句，指定变量的属性，例如`assert(isinstance(text, list))`。如果`text`的值在你的代码被用在一些较大的环境中时变为了一个字符串，将产生一个`AssertionError`，于是你会立即得到问题的通知。\n",
    "\n",
    "一旦你觉得你发现了错误，作为一个假设查看你的解决方案。在重新运行该程序之前尝试预测你修正错误的影响。如果bug不能被修正，不要陷入盲目修改代码希望它会奇迹般地重新开始运作的陷阱。相反，每一次修改都要尝试阐明错误是什么和为什么这样修改会解决这个问题的假设。如果这个问题没有解决就撤消这次修改。\n",
    "\n",
    "当你开发你的程序时，扩展其功能，并修复所有bug，维护一套测试用例是有益的。这被称为回归测试，因为它是用来检测代码“回归”的地方——修改代码后会带来一个意想不到的副作用是以前能运作的程序不运作了的地方。Python以`doctest`模块的形式提供了一个简单的回归测试框架。这个模块搜索一个代码或文档文件查找类似与交互式Python会话这样的文本块，这种形式你已经在这本书中看到了很多次。它执行找到的Python命令，测试其输出是否与原始文件中所提供的输出匹配。每当有不匹配时，它会报告预期值和实际值。有关详情，请查询在 documentation at `http://docs.python.org/library/doctest.html`上的`doctest`文档。除了回归测试它的值，`doctest`模块有助于确保你的软件文档与你的代码保持同步。\n",
    "\n",
    "也许最重要的防御性编程策略是要清楚的表述你的代码，选择有意义的变量和函数名，并通过将代码分解成拥有良好文档的接口的函数和模块尽可能的简化代码。\n",
    "\n",
    "## 4.7 算法设计\n",
    "\n",
    "<a href=\"#递归\">1. 递归</a>\n",
    "\n",
    "<a href=\"#权衡空间与时间\">2. 权衡空间与时间</a>\n",
    "\n",
    "<a href=\"#动态规划\">3. 动态规划</a>\n",
    "\n",
    "<a href=\"#networkx\">4. NetworkX</a>\n",
    "\n",
    "<a href=\"#csv\">5. csv</a>\n",
    "\n",
    "<a href=\"#numpy\">6. NumPy</a>\n",
    "\n",
    "本节将讨论更高级的概念，你在第一次阅读本章时可能更愿意跳过本节。\n",
    "\n",
    "解决算法问题的一个重要部分是为手头的问题选择或改造一个合适的算法。有时会有几种选择，能否选择最好的一个取决于对每个选择随数据增长如何执行的知识。关于这个话题的书很多，我们只介绍一些关键概念和精心描述在自然语言处理中最普遍的做法。\n",
    "\n",
    "最有名的策略被称为分而治之。我们解决一个大小为*n*的问题通过将其分成两个大小为*n/2*的问题，解决这些问题，组合它们的结果成为原问题的结果。例如，假设我们有一堆卡片，每张卡片上写了一个词。我们可以排序这一堆卡片，通过将它分成两半分别给另外两个人来排序（他们又可以做同样的事情）。然后，得到两个排好序的卡片堆，将它们并成一个单一的排序堆就是一项容易的任务了。参见[4.8](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#fig-mergesort)这个过程的说明。\n",
    "\n",
    "![1094084b61ac3f0e4416e92869c52ccd.png](attachment:1094084b61ac3f0e4416e92869c52ccd.png)\n",
    "\n",
    "图 4.8：通过分而治之排序：对一个数组排序，我们将其分成两半并对每一半进行排序（递归）；将每个排好序的一半合并成一个完整的链表（再次递归）；这个算法被称为“归并排序“。\n",
    "\n",
    "另一个例子是在词典中查找一个词的过程。我们打开在书的中部附近的一个地方，比较我们的词与当前页面上的词。如果它在词典中的词前面，我们就在词典的前一半重复上面的过程；如果它在后面，我们就使用词典的后一半。这种搜索方法被称为二分查找，因为它的每一步都将问题分裂成一半。\n",
    "\n",
    "算法设计的另一种方法，我们解决问题通过将它转化为一个我们已经知道如何解决的问题的一个实例。例如，为了检测列表中的重复项，我们可以预排序这个列表，然后通过一次扫描检查是否有相邻的两个元素是相同的。\n",
    "\n",
    "### 递归\n",
    "\n",
    "上面的关于排序和搜索的例子有一个引人注目的特征：解决一个大小为n的问题，可以将其分成两半，然后处理一个或多个大小为n/2的问题。实现这种方法的一种常见方式是使用递归。我们定义一个函数f，从而简化了问题，并调用自身来解决一个或多个同样问题的更简单的实例。然后组合它们的结果成为原问题的解答。\n",
    "\n",
    "例如，假设我们有n个词，要计算出它们结合在一起有多少不同的方式能组成一个词序列。如果我们只有一个词（n=1），只是一种方式组成一个序列。如果我们有2个词，就有2种方式将它们组成一个序列。3个词有6种可能性。一般的，n个词有n × n-1 × … × 2 × 1种方式（即n的阶乘）。我们可以将这些编写成如下代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b079e4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def factorial1(n): # 一个跑了两分钟还不结束的代码\n",
    "    result = 1\n",
    "    for i in range(n):\n",
    "        result *= (i+1)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4858ee4",
   "metadata": {},
   "source": [
    "但是，也可以使用一种递归算法来解决这个问题，该算法基于以下观察。假设我们有办法为n-1 不同的词构建所有的排列。然后对于每个这样的排列，有n个地方我们可以插入一个新词：开始、结束或任意两个词之间的n-2个空隙。因此，我们简单的将n-1个词的解决方案数乘以n的值。我们还需要基础案例，也就是说，如果我们有一个词，只有一个顺序。我们可以将这些编写成如下代码："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4cac53e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def factorial2(n):\n",
    "    if n == 1:\n",
    "        return 1\n",
    "    else:\n",
    "        return n * factorial2(n-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7174347e",
   "metadata": {},
   "source": [
    "这两种算法解决同样的问题。一个使用迭代，而另一个使用递归。我们可以用递归处理深层嵌套的对象，例如WordNet的上位词层次。让我们计数给定同义词集s为根的上位词层次的大小。我们会找到s的每个下位词的大小，然后将它们加到一起（我们也将加1 表示同义词集本身）。下面的函数`size1()`做这项工作；注意函数体中包括`size1()`的递归调用："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "69165577",
   "metadata": {},
   "outputs": [],
   "source": [
    "def size1(s):\n",
    "    return 1 + sum(size1(child) for child in s.hyponyms())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6154174e",
   "metadata": {},
   "source": [
    "我们也可以设计一种这个问题的迭代解决方案处理层的层次结构。第一层是同义词集本身 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#first-layer)，然后是同义词集所有的下位词，之后是所有下位词的下位词。每次循环通过查找上一层的所有下位词计算下一层 [# 3](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#update-layer)。它也保存了到目前为止遇到的同义词集的总数 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#update-total)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b50e932c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def size2(s):\n",
    "    layer = [s] \n",
    "    total = 0\n",
    "    while layer:\n",
    "        total += len(layer) \n",
    "        layer = [h for c in layer for h in c.hyponyms()] \n",
    "    return total"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d3c0613",
   "metadata": {},
   "source": [
    "迭代解决方案不仅代码更长而且更难理解。它迫使我们程序式的思考问题，并跟踪`layer`和`total`随时间变化发生了什么。让我们满意的是两种解决方案均给出了相同的结果。我们将使用import语句的一个新的形式，允许我们缩写名称`wordnet`为`wn`："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3f9408ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "190"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk.corpus import wordnet as wn\n",
    "dog = wn.synset('dog.n.01')\n",
    "size1(dog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "07f3b223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "190"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size2(dog)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "167024b4",
   "metadata": {},
   "source": [
    "作为递归的最后一个例子，让我们用它来构建一个深嵌套的对象。一个字母查找树是一种可以用来索引词汇的数据结构，一次一个字母。（这个名字来自于单词retrieval）。例如，如果`trie`包含一个字母的查找树，那么`trie['c']`是一个较小的查找树，包含所有以c开头的词。[4.9](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-trie)演示了使用Python 字典（[3](https://usyiyi.github.io/nlp-py-2e-zh/ch05.html#sec-dictionaries)）构建查找树的递归过程。若要插入词chien（dog的法语），我们将c分类，递归的掺入hien到`trie['c']`子查找树中。递归继续直到词中没有剩余的字母，于是我们存储的了预期值（本例中是词dog）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "050a4a7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def insert(trie, key, value):\n",
    "    if key:\n",
    "        first, rest = key[0], key[1:]\n",
    "        if first not in trie:\n",
    "            trie[first] = {}\n",
    "        insert(trie[first], rest, value)\n",
    "    else:\n",
    "        trie['value'] = value"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "44ab05cf",
   "metadata": {},
   "source": [
    "小心！\n",
    "\n",
    "尽管递归编程结构简单，但它是有代价的。每次函数调用时，一些状态信息需要推入堆栈，这样一旦函数执行完成可以从离开的地方继续执行。出于这个原因，迭代的解决方案往往比递归解决方案的更高效。\n",
    "\n",
    "### 权衡空间与时间\n",
    "\n",
    "我们有时可以显著的加快程序的执行，通过建设一个辅助的数据结构，例如索引。[4.10](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-search-documents)实现一个简单的电影评论语料库的全文检索系统。通过索引文档集合，它提供更快的查找。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "578b3944",
   "metadata": {},
   "outputs": [],
   "source": [
    "def raw(file):\n",
    "    contents = open(file).read()\n",
    "    contents = re.sub(r'<.*?>', ' ', contents)\n",
    "    contents = re.sub('\\s+', ' ', contents)\n",
    "    return contents\n",
    "\n",
    "def snippet(doc, term):\n",
    "    text = ' '*30 + raw(doc) + ' '*30\n",
    "    pos = text.index(term)\n",
    "    return text[pos-30:pos+30]\n",
    "\n",
    "print(\"Building Index\")\n",
    "files = nltk.corpus.movie_reviews.abspaths()\n",
    "idx = nltk.Index((w, f) for f in files for w in raw(f).split())\n",
    "\n",
    "query = ''\n",
    "while query != \"quit\":\n",
    "    query = input(\"query> \")     # use raw_input() in Python 2\n",
    "    if query in idx:\n",
    "        for doc in idx[query]:\n",
    "            print(snippet(doc, query))\n",
    "    else:\n",
    "        print(\"Not found\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9a773be9",
   "metadata": {},
   "source": [
    "一个更微妙的空间与时间折中的例子涉及使用整数标识符替换一个语料库的词符。我们为语料库创建一个词汇表，每个词都被存储一次的列表，然后转化这个列表以便我们能通过查找任意词来找到它的标识符。每个文档都进行预处理，使一个词列表变成一个整数列表。现在所有的语言模型都可以使用整数。见[4.11](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-strings-to-ints)中的内容，如何为一个已标注的语料库做这个的例子的列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9291d341",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(tagged_corpus):\n",
    "    words = set()\n",
    "    tags = set()\n",
    "    for sent in tagged_corpus:\n",
    "        for word, tag in sent:\n",
    "            words.add(word)\n",
    "            tags.add(tag)\n",
    "    wm = dict((w, i) for (i, w) in enumerate(words))\n",
    "    tm = dict((t, i) for (i, t) in enumerate(tags))\n",
    "    return [[(wm[w], tm[t]) for (w, t) in sent] for sent in tagged_corpus]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a1e98c57",
   "metadata": {},
   "source": [
    "空间时间权衡的另一个例子是维护一个词汇表。如果你需要处理一段输入文本检查所有的词是否在现有的词汇表中，词汇表应存储为一个集合，而不是一个列表。集合中的元素会自动索引，所以测试一个大的集合的成员将远远快于测试相应的列表的成员。\n",
    "\n",
    "我们可以使用`timeit`模块测试这种说法。`Timer`类有两个参数：一个是多次执行的语句，一个是只在开始执行一次的设置代码。我们将分别使用一个整数的列表 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#vocab-list)和一个整数的集合 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#vocab-set)模拟10 万个项目的词汇表。测试语句将产生一个随机项，它有50％的机会在词汇表中 [# 3](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#vocab-statement)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "029ebf8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0008371999997507373\n"
     ]
    }
   ],
   "source": [
    "from timeit import Timer\n",
    "vocab_size = 100000\n",
    "setup_list = \"import random; vocab = range(%d)\" % vocab_size \n",
    "setup_set = \"import random; vocab = set(range(%d))\" % vocab_size \n",
    "statement = \"random.randint(0, %d) in vocab\" % (vocab_size * 2) \n",
    "print(Timer(statement, setup_list).timeit(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "5f670fa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0008661000001666253\n"
     ]
    }
   ],
   "source": [
    "print(Timer(statement, setup_set).timeit(1000))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9421c795",
   "metadata": {},
   "source": [
    "执行1000 次链表成员资格测试总共需要2.8秒，而在集合上的等效试验仅需0.0037 秒，也就是说快了三个数量级！\n",
    "\n",
    "### 动态规划\n",
    "\n",
    "动态规划是一种自然语言处理中被广泛使用的算法设计的一般方法。“programming”一词的用法与你可能想到的感觉不同，是规划或调度的意思。动态规划用于解决包含多个重叠的子问题的问题。不是反复计算这些子问题，而是简单的将它们的计算结果存储在一个查找表中。在本节的余下部分，我们将介绍动态规划，在一个相当不同的背景下来句法分析。\n",
    "\n",
    "Pingala是大约生活在公元前5世纪的印度作家，作品有被称为*《Chandas Shastra》*的梵文韵律专著。Virahanka大约在公元6 世纪延续了这项工作，研究短音节和长音节组合产生一个长度为*n*的旋律的组合数。短音节，标记为*S*，占一个长度单位，而长音节，标记为*L*，占2个长度单位。例如，Pingala 发现，有5 种方式构造一个长度为4 的旋律：*V*4 = *{LL, SSL, SLS, LSS, SSSS}*。请看，我们可以将*V*4分成两个子集，以*L*开始的子集和以*S*开始的子集，如[(1)](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#ex-v4)所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8096dc42",
   "metadata": {},
   "outputs": [],
   "source": [
    "V4 =\n",
    "  LL, LSS\n",
    "    i.e. L prefixed to each item of V2 = {L, SS}\n",
    "  SSL, SLS, SSSS\n",
    "    i.e. S prefixed to each item of V3 = {SL, LS, SSS}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "878af55b",
   "metadata": {},
   "source": [
    "有了这个观察结果，我们可以写一个小的递归函数称为`virahanka1()`来计算这些旋律，如[4.12](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-virahanka)所示。请注意，要计算*V*4，我们先要计算*V*3和*V*2。但要计算*V*3，我们先要计算*V*2和*V*1。在[(2)](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#ex-call-structure)中描述了这种调用结构。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8b76e382",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import arange\n",
    "from matplotlib import pyplot\n",
    "\n",
    "colors = 'rgbcmyk' # red, green, blue, cyan, magenta, yellow, black\n",
    "\n",
    "def bar_chart(categories, words, counts):\n",
    "    \"Plot a bar chart showing counts for each word by category\"\n",
    "    ind = arange(len(words))\n",
    "    width = 1 / (len(categories) + 1)\n",
    "    bar_groups = []\n",
    "    for c in range(len(categories)):\n",
    "        bars = pyplot.bar(ind+c*width, counts[categories[c]], width,\n",
    "                         color=colors[c % len(colors)])\n",
    "        bar_groups.append(bars)\n",
    "    pyplot.xticks(ind+width, words)\n",
    "    pyplot.legend([b[0] for b in bar_groups], categories, loc='upper left')\n",
    "    pyplot.ylabel('Frequency')\n",
    "    pyplot.title('Frequency of Six Modal Verbs by Genre')\n",
    "    pyplot.show()"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "id": "8a265e89",
   "metadata": {},
   "source": [
    "![2ce816f11fd01927802253d100780b0a.png](attachment:2ce816f11fd01927802253d100780b0a.png)\n",
    "\n",
    "图 4.14：条形图显示布朗语料库中不同部分的情态动词频率：这个可视化图形由[4.13](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-modal-plot)中的程序产生。\n",
    "\n",
    "从该柱状图可以立即看出may和must有几乎相同的相对频率。could和might也一样。\n",
    "\n",
    "也可以动态的产生这些数据的可视化图形。例如，一个使用表单输入的网页可以允许访问者指定搜索参数，提交表单，看到一个动态生成的可视化图形。要做到这一点，我们必须为`matplotlib`指定`Agg`后台，它是一个产生栅格（像素）图像的库 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#agg-backend)。下一步，我们像以前一样使用相同的Matplotlib 方法，但不是用`pyplot.show()`显示结果在图形终端，而是使用`pyplot.savefig()`把它保存到一个文件 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#pyplot-savefig)。我们指定文件名，然后输出一些HTML标记指示网页浏览器来加载该文件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "1debabdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content-Type: text/html\n",
      "\n",
      "<html><body>\n",
      "<img src=\"modals.png\"/>\n",
      "</body></html>\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import use, pyplot\n",
    "use('Agg') \n",
    "pyplot.savefig('modals.png') \n",
    "print('Content-Type: text/html')\n",
    "print()\n",
    "print('<html><body>')\n",
    "print('<img src=\"modals.png\"/>')\n",
    "print('</body></html>')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "682f7e13",
   "metadata": {},
   "source": [
    "### NetworkX\n",
    "\n",
    "NetworkX包定义和操作被称为图的由节点和边组成的结构。它可以从`https://networkx.lanl.gov/`得到。NetworkX可以和Matplotlib 结合使用可视化如WordNet的网络结构（语义网络，我们在[5](https://usyiyi.github.io/nlp-py-2e-zh/ch02.html#sec-wordnet)介绍过）。[4.15](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-networkx)中的程序初始化一个空的图 [# 3](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#define-graph)，然后遍历WordNet上位词层次为图添加边 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#add-edge)。请注意，遍历是递归的 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#recursive-traversal)，使用在[4.7](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#sec-algorithm-design)讨论的编程技术。结果显示在[4.16](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#fig-dog-graph)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "25e1f1b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib\n",
    "from nltk.corpus import wordnet as wn\n",
    "\n",
    "def traverse(graph, start, node):\n",
    "    graph.depth[node.name] = node.shortest_path_distance(start)\n",
    "    for child in node.hyponyms():\n",
    "        graph.add_edge(node.name, child.name) \n",
    "        traverse(graph, start, child) \n",
    "\n",
    "def hyponym_graph(start):\n",
    "    G = nx.Graph() \n",
    "    G.depth = {}\n",
    "    traverse(G, start, start)\n",
    "    return G\n",
    "\n",
    "def graph_draw(graph):\n",
    "    nx.draw_graphviz(graph,\n",
    "         node_size = [16 * graph.degree(n) for n in graph],\n",
    "         node_color = [graph.depth[n] for n in graph],\n",
    "         with_labels = False)\n",
    "    matplotlib.pyplot.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "59cf821e",
   "metadata": {},
   "source": [
    "![Images/dog-graph.png](https://usyiyi.github.io/nlp-py-2e-zh/Images/8cb61a943f3d34f94596e77065410cd3.jpg)\n",
    "\n",
    "图 4.16：使用NetworkX和Matplotlib可视化数据：WordNet的上位词层次的部分显示，开始于`dog.n.01`（中间最黑的节点）；节点的大小对应节点的孩子的数目，颜色对应节点到`dog.n.01`的距离；此可视化图形由[4.15](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#code-networkx)中的程序产生。\n",
    "\n",
    "### csv\n",
    "\n",
    "语言分析工作往往涉及数据统计表，包括有关词项的信息、试验研究的参与者名单或从语料库提取的语言特征。这里有一个CSV格式的简单的词典片段：\n",
    "\n",
    "sleep, sli:p, v.i, a condition of body and mind \n",
    "\n",
    "walk, wo:k, v.intr, progress by lifting and setting down each foot \n",
    "\n",
    "wake, weik, intrans, cease to sleep\n",
    "\n",
    "我们可以使用Python的CSV库读写这种格式存储的文件。例如，我们可以打开一个叫做`lexicon.csv`的CSV 文件 [# 1](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#open-csv)，并遍历它的行 [# 2](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#iterate-csv)："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08030bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "input_file = open(\"lexicon.csv\", \"rb\") \n",
    "for row in csv.reader(input_file): \n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460065db",
   "metadata": {},
   "source": [
    "每一行是一个字符串列表。如果字段包含有数值数据，它们将作为字符串出现，所以都必须使用`int()`或`float()`转换。\n",
    "\n",
    "### NumPy\n",
    "\n",
    "NumPy包对Python中的数值处理提供了大量的支持。NumPy有一个多维数组对象，它可以很容易初始化和访问："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "f1099bf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import array\n",
    "cube = array([ [[0,0,0], [1,1,1], [2,2,2]],\n",
    "               [[3,3,3], [4,4,4], [5,5,5]],\n",
    "               [[6,6,6], [7,7,7], [8,8,8]] ])\n",
    "cube[1,1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "159c2d03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 7, 8],\n",
       "       [6, 7, 8],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube[2].transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "b591e048",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 7, 7],\n",
       "       [8, 8, 8]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube[2,1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "458729b9",
   "metadata": {},
   "source": [
    "NumPy包括线性代数函数。在这里我们进行矩阵的奇异值分解，潜在语义分析中使用的操作，它能帮助识别一个文档集合中的隐含概念。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "73d530ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.4472136 , -0.89442719],\n",
       "       [-0.89442719,  0.4472136 ]])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import linalg\n",
    "a=array([[4,0], [3,-5]])\n",
    "u,s,vt = linalg.svd(a)\n",
    "u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "3626d87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.32455532, 3.16227766])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "2401727c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.70710678,  0.70710678],\n",
       "       [-0.70710678, -0.70710678]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "45687d54",
   "metadata": {},
   "source": [
    "NLTK中的聚类包`nltk.cluster`中广泛使用NumPy数组，支持包括k-means聚类、高斯EM聚类、组平均凝聚聚类以及聚类分析图。有关详细信息，请输入`help(nltk.cluster)`。\n",
    "\n",
    "### 其他Python库\n",
    "\n",
    "还有许多其他的Python库，你可以使用`http://pypi.python.org/`处的Python 包索引找到它们。许多库提供了外部软件接口，例如关系数据库（如`mysql-python`）和大数据集合（如`PyLucene`）。许多其他库能访问各种文件格式，如PDF、MSWord 和XML（`pypdf`, `pywin32`, `xml.etree`）、RSS 源（如`feedparser`）以及电子邮箱（如`imaplib`, `email`）。\n",
    "\n",
    "## 6 小结\n",
    "\n",
    "- Python 赋值和参数传递使用对象引用，例如如果`a`是一个列表，我们分配`b = a`，然后任何`a`上的操作都将修改`b`，反之亦然。\n",
    "- `is`操作测试是否两个对象是相同的内部对象，而`==`测试是否两个对象是相等的。两者的区别和词符与词类型的区别相似。\n",
    "- 字符串、列表和元组是不同类型的序列对象，支持常见的操作如：索引、切片、`len()`、`sorted()`和使用`in`的成员测试。\n",
    "- 声明式的编程风格通常会产生更简洁更可读的代码；手动递增循环变量通常是不必要的；枚举一个序列，使用`enumerate()`。\n",
    "- 函数是一个重要的编程抽象，需要理解的关键概念有：参数传递、变量的作用域和文档字符串。\n",
    "- 函数作为一个命名空间：函数内部定义的名称在该函数外不可见，除非这些名称被宣布为是全局的。\n",
    "- 模块允许将材料与本地的文件逻辑的关联起来。一个模块作为一个命名空间：在一个模块中定义的名称——如变量和函数——在其他模块中不可见，除非这些名称被导入。\n",
    "- 动态规划是一种在NLP 中广泛使用的算法设计技术，它存储以前的计算结果，以避免不必要的重复计算。\n",
    "\n",
    "## 4.10 深入阅读\n",
    "\n",
    "本章已经触及编程中的许多主题，一些是Python特有的，一些是相当普遍的。我们只是触及了表面，你可能想要阅读更多有关这些主题的内容，可以从`http://nltk.org/`处的本章深入阅读材料开始。\n",
    "\n",
    "Python网站提供大量文档。理解内置的函数和标准类型是很重要的，在`http://docs.python.org/library/functions.html`和`http://docs.python.org/library/stdtypes.html`处有描述。我们已经学习了生成器以及它们对提高效率的重要性；关于迭代器的信息，一个密切相关的话题请看`http://docs.python.org/library/itertools.html`。查询你喜欢的Python书籍中这些主题的更多信息。使用Python进行多媒体处理包括声音文件的一个优秀的资源是[(Guzdial, 2005)](https://usyiyi.github.io/nlp-py-2e-zh/bibliography.html#guzdial2005)。\n",
    "\n",
    "使用在线Python文档时，要注意你安装的版本可能与你正在阅读的文档的版本不同。你可以使用`import sys; sys.version`松地查询你有的是什么版本。特定版本的文档在`http://www.python.org/doc/versions/`处。\n",
    "\n",
    "算法设计是计算机科学中一个丰富的领域。一些很好的出发点是[(Harel, 2004)](https://usyiyi.github.io/nlp-py-2e-zh/bibliography.html#harel2004), [(Levitin, 2004)](https://usyiyi.github.io/nlp-py-2e-zh/bibliography.html#levitin2004), [(Knuth, 2006)](https://usyiyi.github.io/nlp-py-2e-zh/bibliography.html#knuth2006trees)。[(Hunt & Thomas, 2000)](https://usyiyi.github.io/nlp-py-2e-zh/bibliography.html#hunt2000)和[(McConnell, 2004)](https://usyiyi.github.io/nlp-py-2e-zh/bibliography.html#mcconnell2004)为软件开发实践提供了有益的指导。\n",
    "\n",
    "## 4.11 练习\n",
    "\n",
    "1. ☼ 使用Python的帮助功能，找出更多关于序列对象的内容。在解释器中输入`help(str)`，`help(list)`和`help(tuple)`。这会给你一个每种类型支持的函数的完整列表。一些函数名字有些特殊，两侧有下划线；正如帮助文档显示的那样，每个这样的函数对应于一些较为熟悉的东西。例如`x.__getitem__(y)`仅仅是以长篇大论的方式使用`x[y]`。\n",
    "\n",
    "2. ☼ 确定三个同时在元组和链表上都可以执行的操作。确定三个不能在元组上执行的列表操作。命名一段使用列表替代元组会产生一个Python错误的代码。\n",
    "\n",
    "3. ☼ 找出如何创建一个由单个项目组成的元组。至少有两种方法可以做到这一点。\n",
    "\n",
    "4. ☼ 创建一个列表`words = ['is', 'NLP', 'fun', '?']`。使用一系列赋值语句（如`words[1] = words[2]`）和临时变量`tmp`将这个列表转换为列表`['NLP', 'is', 'fun', '!']`。现在，使用元组赋值做相同的转换。\n",
    "\n",
    "5. ☼ 通过输入`help(cmp)`阅读关于内置的比较函数`cmp`的内容。它与比较运算符在行为上有何不同？\n",
    "\n",
    "6. ☼ 创建一个n-grams的滑动窗口的方法在下面两种极端情况下是否正确：n = 1 和n = `len(sent)`？\n",
    "\n",
    "7. ☼ 我们指出当空字符串和空链表出现在`if`从句的条件部分时，它们的判断结果是`False`。在这种情况下，它们被说成出现在一个布尔上下文中。实验各种不同的布尔上下文中的非布尔表达式，看它们是否被判断为`True`或`False`。\n",
    "\n",
    "8. ☼ 使用不等号比较字符串，如`'Monty' < 'Python'`。当你做`'Z' < 'a'`时会发生什么？尝试具有共同前缀的字符串对，如`'Monty' < 'Montague'`。阅读有关“字典排序”的内容以便了解这里发生了什么事。尝试比较结构化对象，如`('Monty', 1) < ('Monty', 2)`。这与预期一样吗？\n",
    "\n",
    "9. ☼ 写代码删除字符串开头和结尾处的空白，并规范化词之间的空格为一个单独的空格字符。\n",
    "\n",
    "   1. 使用`split()`和`join()`做这个任务\n",
    "   2. 使用正则表达式替换做这个任务\n",
    "\n",
    "10. ☼ 写一个程序按长度对词排序。定义一个辅助函数`cmp_len`，它在词长上使用`cmp`比较函数。\n",
    "\n",
    "11. ◑ 创建一个词列表并将其存储在变量`sent1`。现在赋值`sent2 = sent1`。修改`sent1`中的一个项目，验证`sent2`改变了。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6854409",
   "metadata": {},
   "source": [
    "1. 现在尝试同样的练习，但使用`sent2 = sent1[:]`赋值。再次修改`sent1`看看`sent2`会发生什么。解释。\n",
    "2. 现在定义`text1`为一个字符串列表的列表（例如表示由多个句子组成的文本）。现在赋值`text2 = text1[:]`，分配一个新值给其中一个词，例如`text1[1][1] = 'Monty'`。检查这对`text2`做了什么。解释。\n",
    "3. 导入Python的`deepcopy()`函数（即`from copy import deepcopy`），查询其文档，使用它生成任一对象的新副本。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692e1347",
   "metadata": {},
   "source": [
    "12. ◑ 使用列表乘法初始化*n*-by-*m*的空字符串列表的咧表，例如`word_table = [[''] * n] * m`。当你设置其中一个值时会发生什么事，例如`word_table[1][2] = \"hello\"`？解释为什么会出现这种情况。现在写一个表达式，使用`range()`构造一个列表，表明它没有这个问题。\n",
    "\n",
    "13. ◑ 写代码初始化一个称为`word_vowels`的二维数组的集合，处理一个词列表，添加每个词到`word_vowels[l][v]`，其中`l`是词的长度，`v`是它包含的元音的数量。\n",
    "\n",
    "14. ◑ 写一个函数`novel10(text)`输出所有在一个文本最后10%出现而之前没有遇到过的词。\n",
    "\n",
    "15. ◑ 写一个程序将一个句子表示成一个单独的字符串，分割和计数其中的词。让它输出每一个词和词的频率，每行一个，按字母顺序排列。\n",
    "\n",
    "16. ◑ 阅读有关Gematria的内容，它是一种方法，分配一个数字给词汇，具有相同数字的词之间映射以发现文本隐藏的含义（`http://en.wikipedia.org/wiki/Gematria`, `http://essenes.net/gemcal.htm`）。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61cf9bd4",
   "metadata": {},
   "source": [
    "1. 写一个函数`gematria()`，根据`letter_vals`中的字母值，累加一个词的字母的数值：\n",
    "\n",
    "   ```\n",
    "   letter_vals = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5, 'f':80, 'g':3, 'h':8,\n",
    "   'i':10, 'j':10, 'k':20, 'l':30, 'm':40, 'n':50, 'o':70, 'p':80, 'q':100,\n",
    "   'r':200, 's':300, 't':400, 'u':6, 'v':6, 'w':800, 'x':60, 'y':10, 'z':7}\n",
    "   ```\n",
    "\n",
    "2. 处理一个语料库（`nltk.corpus.state_union`）对每个文档计数它有多少词的字母数值为666。\n",
    "\n",
    "3. 写一个函数`decode()`来处理文本，随机替换词汇为它们的Gematria等值的词，以发现文本的“隐藏的含义”。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "93c8ddae",
   "metadata": {},
   "source": [
    "17. ◑ 写一个函数`shorten(text, n)`处理文本，省略文本中前n个最频繁出现的词。它的可读性会如何？\n",
    "\n",
    "18. ◑ 写代码输出词汇的索引，允许别人根据其含义查找词汇（或它们的发言；词汇条目中包含的任何属性）。\n",
    "\n",
    "19. ◑ 写一个列表推导排序WordNet中与给定同义词集接近的同义词集的列表。例如，给定同义词集`minke_whale.n.01`, `orca.n.01`, `novel.n.01`和`tortoise.n.01`，按照它们与`right_whale.n.01`的`shortest_path_distance()`对它们进行排序。\n",
    "\n",
    "20. ◑ 写一个函数处理一个词列表（含重复项），返回一个按照频率递减排序的词列表（没有重复项）。例如如果输入列表中包含词`table`的10个实例，`chair`的9个实例，那么在输出列表中`table`会出现在`chair`前面。\n",
    "\n",
    "21. ◑ 写一个函数以一个文本和一个词汇表作为它的参数，返回在文本中出现但不在词汇表中的一组词。两个参数都可以表示为字符串列表。你能使用`set.difference()`在一行代码中做这些吗？\n",
    "\n",
    "22. ◑ 从Python标准库的`operator`模块导入`itemgetter()`函数（即`from operator import itemgetter`）。创建一个包含几个词的列表`words`。现在尝试调用：`sorted(words, key=itemgetter(1))`和`sorted(words, key=itemgetter(-1))`。解释`itemgetter()`正在做什么。\n",
    "\n",
    "23. ◑ 写一个递归函数`lookup(trie, key)`在查找树中查找一个关键字，返回找到的值。扩展这个函数，当一个词由其前缀唯一确定时返回这个词（例如`vanguard`是以`vang-`开头的唯一的词，所以`lookup(trie, 'vang')`应该返回与`lookup(trie, 'vanguard')`相同的内容）。\n",
    "\n",
    "24. ◑ 阅读关于“关键字联动”的内容（[(Scott & Tribble, 2006)](https://usyiyi.github.io/nlp-py-2e-zh/bibliography.html#scott2006)的第5章）。从NLTK的莎士比亚语料库中提取关键字，使用NetworkX包，画出关键字联动网络。\n",
    "\n",
    "25. ◑ 阅读有关字符串编辑距离和Levenshtein 算法的内容。尝试`nltk.edit_distance()`提供的实现。这用的是动态规划的何种方式？它使用的是自下而上还是自上而下的方法？[另见`http://norvig.com/spell-correct.html`]\n",
    "\n",
    "26. ◑ Catalan 数出现在组合数学的许多应用中，包括解析树的计数（[6](https://usyiyi.github.io/nlp-py-2e-zh/ch08.html#sec-grammar-development)）。该级数可以定义如下：C0 = 1, and Cn+1 = Σ0..n (CiCn-i)。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a04ca9d",
   "metadata": {},
   "source": [
    "1. 编写一个递归函数计算第n个Catalan数Cn。\n",
    "2. 现在写另一个函数使用动态规划做这个计算。\n",
    "3. 使用`timeit`模块比较当n增加时这些函数的性能。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "818b8aa3",
   "metadata": {},
   "source": [
    "27. ★ 重现有关著作权鉴定的[(Zhao & Zobel, 2007)](https://usyiyi.github.io/nlp-py-2e-zh/bibliography.html#zhao07)中的一些结果。\n",
    "\n",
    "28. ★ 研究性别特异词汇选择，看你是否可以重现一些`http://www.clintoneast.com/articles/words.php`的结果\n",
    "\n",
    "29. ★ 写一个递归函数漂亮的按字母顺序排列输出一个查找树，例如:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91d03601",
   "metadata": {},
   "source": [
    "```\n",
    "chair: 'flesh'\n",
    "---t: 'cat'\n",
    "--ic: 'stylish'\n",
    "---en: 'dog'\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c2924e96",
   "metadata": {},
   "source": [
    "## Docutils System Messages\n",
    "\n",
    "System Message: ERROR/3 (`ch04.rst2`, line 1791); *[backlink](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#id10)*\n",
    "\n",
    "Unknown target name: \"first-run\".\n",
    "\n",
    "System Message: ERROR/3 (`ch04.rst2`, line 1791); *[backlink](https://usyiyi.github.io/nlp-py-2e-zh/ch04.html#id12)*\n",
    "\n",
    "Unknown target name: \"second-run\"."
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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